Stepwise Multiple Regression Research Articles

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms

ABSTRACT Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

A cross-sectional study of academic success measures, grit, and resilience among US dental students.

This study explored themes previously identified as central to holistic professional school success, as related to traditional academic achievement, resilience, and grit measures. This cross-sectional study surveyed third- and fourth-year US dental students. Items addressed themes previously identified as central to professional school success, including need for cognition, patient ownership, collective orientation, grit, and resilience. Traditional academic metrics were also included. Statistical analyses comprised one-way analysis of variance (ANOVA), simple linear regression, and stepwise multiple regression procedures. A total of 126 students participated (81% response rate). Collective orientation, need for cognition, patient ownership, and resilience did not significantly correlate with grade point average (GPA), whereas grit did, r=0.29. Need for cognition and patient ownership combined significantly predicted grit with approximately 35% of variance explained. Approximately 25% of variance in resilience scores was accounted for by need for cognition, coupled with collective orientation. Grit and resilience significantly correlated, r=0.59. Grit significantly predicted both traditional and alternative metrics of success among dental students, particularly need for cognition and patient ownership. A high need for cognition is consistent among students who display both grit and resilience. Neither resilience nor the alternative measures of success predicted traditional academic metrics.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Risk profiles of elite breath-hold divers

ABSTRACT This study aimed to determine a typical profile of elite breath-hold divers (BHDs), in relation to loss of consciousness (LOC) and episodic memory. Forty-four BHDs were evaluated during a world championship with anthropometric and physiological measurements, psychosociological factors and memory assessment. Seventy-five percent of the BHDs had at least one LOC with the predominance being men (p < 0.05). Thirty six percent of BHDs presented a low-risk profile and 64% a high-risk profile with no particular psychological pattern. Stepwise multiple linear regression showed that body fat, years of BH practice, age and forced vital capacity explained a significant amount of the variance of LOC for all BHDs (F(4,39) = 16.03, p < 0.001, R2 = 0.622, R2 Adjusted = 0.583). No correlation was found between resting physiological parameters and their training or depth performances. In conclusion, anthropometric data, pulmonary factors and breath-holding experience were predictive of LOC in elite BHDs, with men taking more risks. BHDs episodic memory was not impaired.

Factors influencing work engagement of nurses in general hospitals: A cross-sectional study.

This study aimed to examine the level of work engagement and to identify the factors influencing the work engagement of nurses. Work engagement plays a crucial role in enhancing the efficiency of outcomes and the standard of care in nursing and healthcare services. A descriptive predictive design and stratified random sampling was used to select206 nurses from three general hospitals in Naypyitaw, Myanmar.. Research instruments consisted of the demographic data form, the Utrecht Work Engagement Scale, the Global Transformational Leadership Scale, the Survey of Perceived Organizational Support, and the Psychological Capital Questionnaire. The results were explored by applying descriptive statistics and stepwise multiple regression. Overall work engagement was at an average level. Transformational leadership and psychological capital could affect work engagement, explaining 19.34% of the total variance; however, perceived organizational support, age, and work experience did not show any impact on it. The findings recommend that nurse administrators should focus on transformational leadership and psychological capital in formulating strategies to increase nurses' work engagement. The outcomes might providebaseline information for nurse administrators and policymakers by formulating management strategies to enhance nursing leaders' awareness, knowledge, and skills about transformational leadership behaviors and to keep nurses holding self-efficacy, hope, optimism, and resilience of psychological capital in healthcare organizations, which ultimately increase nurses' engagement at work.

Weathering the Unforeseen: The Interplay of Resilience and Innovation in Start-Up Performance Amidst Crisis

ABSTRACT Understanding entrepreneurial dynamics during crises increasingly gains scholars’ and practitioners’ attention, highlighting the adaptability and innovation inherent in entrepreneurial ventures. This study investigates the intertwined role of organizational resilience (OR) and business model innovation (BMI) on the performance of startups amidst the COVID-19 pandemic using data from 258 start-ups in Tanzania. It employs a stepwise multiple regression analysis to assess the moderating effect of BMI on the relationship between OR and the start-ups’ performance. The findings suggest that service sector startups exhibited higher performance than those in manufacturing and merchandising. The results show that startups that actively participate in BMI exhibited greater resilience and outperformed their counterparts during the pandemic. The study underscores the imperative for startups to consistently innovate their business models to maintain their competitiveness and success during disruptive moments.

The Effect of Obesity on Repolarization and Other ECG Parameters.

Background: Overweight and obesity are important risk factors in the development of cardiovascular diseases. New repolarization markers, such as the Tpeak-Tend interval and JTpeak intervals, have not yet been profoundly studied in obese patients. The study aims to analyze whether, in patients with obesity and overweight, repolarization markers, including the Tpeak-Tend interval, are prolonged and simultaneously check the frequency of other ECG pathologies in a 12-lead ECG in this group of patients. Methods: A study group consisted of 181 adults (90 females and 91 males) with overweight and first-class obesity. The participants completed a questionnaire, and the ECG was performed and analyzed. Results: When analyzing the classic markers, only QT dispersion was significantly higher in obese people. The Tpeak-Tend parameter (97.08 ms ± 23.38 vs. 89.74 ms ± 12.88, respectively), its dispersion, and JTpeak-JTend parameters were statistically significantly longer in the obese group than in the controls. There were also substantial differences in P-wave, QRS duration, and P-wave dispersion, which were the highest in obese people. Tpeak-Tend was positively correlated with body mass and waist circumference, while JTpeak was with BMI, hip circumference, and WHR. Tpeak/JT was positively correlated with WHR and BMI. In backward stepwise multiple regression analysis for JTpeak-WHR, type 2 diabetes and smoking had the highest statistical significance. Conclusions: Only selected repolarization markers are significantly prolonged in patients with class 1 obesity and, additionally, in this group, we identified more pathologies of P wave as well as prolonged QRS duration.

Does fear of compassion effect nurses' caring behaviours? a cross-sectional study.

Aims: The aim of this study is to determine the levels of nurses' fear of compassion for others, fear of compassion from others, and fear of self-compassion and to examine the effect of fear of compassion on caring behaviors. Design: A cross-sectional, quantitative design was used. Participants and research context: The study was conducted between October 2022 and April 2023 with 304 nurses working in two public hospitals. Data collection tools were the "Fears of Compassion Scales" and the "Caring Behaviors Inventory." Data were analyzed using a t test, one-way ANOVA, Pearson correlation analysis, and stepwise multiple regression model. Ethical considerations: Ethics committee approval of the research was obtained from the non-invasive ethics committee of Kütahya Health Sciences University (Reference No: 25.05.2022/2022/06-19). The principles of the Declaration of Helsinki were followed in the study. The purpose of the research was explained to all participants, and their verbal/written informed consent was obtained. Results: The mean scores of nurses on fear of compassion for others and from others were at a moderate level, and their scores on fear of self-compassion were close to a moderate level. It was found that the level of nurses' fear of compassion was related to their sociodemographic and professional variables. Fear of self-compassion, fear of compassion for others, and fear of compassion from others explained 33.5% of the total variance in caring behaviors. Conclusions: Nurses' caring behaviors were shown to be more associated with fear of self-compassion than fear of compassion for others. Fear of compassion may be one of the barriers to compassionate care. Interventions that will reduce nurses' fear of compassion may be one way to provide compassionate care. It is recommended to conduct awareness studies on self-compassion and accepting compassion from others in nurses.

Assessment of Nursing Faculty Members' Knowledge Toward Research: A Cross-Sectional Study.

Background and aim In the context of contributing to healthcare and the well-being of a nation and its communities, nursing research plays a vital role in advancing the discipline of nursing science. Nursing research is essential for improving the quality of nursing care. This study aims to examine the research knowledge level of faculty members at the College of Nursing, University of Raparin, Kurdistan Region, Iraq. Methods We conducted a cross-sectional study involving 43 nursing faculty members at the University of Raparin. The survey was distributed using convenience sampling in April 2024. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (released 2019, IBM Corp., Armonk, NY). Continuous variables were analyzed using mean and standard deviation, while categorical variables were analyzed using frequency and percentage distributions. The association between knowledge scores and demographics was tested using Mann-Whitney, Kruskal-Wallis, Chi-square, and post-hoc tests. Stepwise multiple regression analysis was used to determine the variables that influence the knowledge score. Significance was set at p < 0.05. Results A total of 43 participants were enrolled in this study. The mean age was 32.56 ± 4.75, with 69.8% of participants being male. In terms of educational attainment, 48.8% held master's degrees, 39.5% held bachelor's degrees, and only 11% possessed PhDs. The mean work experience was 5.40 ± 4.04 years, and the mean knowledge score toward research was 3.09 ± 1.97 out of 7. Demographic variables, such as age, marital status, and gender, showed no significant associations with knowledge levels (p > 0.05). However, significant associations were found for education level (p = 0.004) and years of experience (p = 0.011). In the stepwise regression analysis, we observed a significant positive correlation between the level of education and knowledge score (F =10.787, p = 0.002). However, variables, such as age, gender, marital status, years of experience, and participation in research courses, did not demonstrate statistical significance (p-values > 0.05). Conclusion This study highlights a significant lack of research expertise among nursing faculty members, emphasizing the urgent need for targeted interventions and instructional activities in nursing education programs. It is crucial to address this knowledge gap in order to support the professional growth of faculty members and promote the advancement of nursing research and evidence-based practice.Policymakers should also consider implementing mentorship programs that strategically pair less experienced faculty members with seasoned researchers. This initiative aims to cultivate a collaborative learning environment and enhance research skills.

Developing and validating a predictive model of delivering large-for-gestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants. AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA. METHODS The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was &lt; 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value &lt; 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses. RESULTS After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P &lt; 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram’s prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit. CONCLUSION Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

유아의 사회정서적 유능감과 자조기술, 유아-교사의 상호작용과의 관계 및 영향

Objectives The purpose of this study is to investigate the relationship and influence of children's social-emotional competence, self-help skills, and child-teacher interaction. Methods The subjects of the study were 140 3-year-old children attending a kindergarten in Gyeonggi-do. Data were collected through questionnaires, and correlation analysis and stepwise multiple regression analysis were performed to analyze the collected data. Results First, it appears that there is a positive correlation between children's social-emotional competence, self-help skills, and child-teacher interaction, and the higher the child's social-emotional competence, the higher the self-help skills and child-teacher interaction. Second, children's social-emotional competence was found to have a significant impact on self-help skills, and among the sub-factors of children's social-emotional competence, emotional recognition and understanding, emotional expression, emotional regulation, social understanding, interpersonal skills, and social problem solving. It was found to have a positive effect on. And among the sub-factors of children's social-emotional competence, emotional recognition and understanding, emotional expression, emotional regulation, social understanding, interpersonal skills, and social problem solving were found to have a positive effect on the child-teacher interaction. Conclusions Through this study, we were able to confirm the importance of developing children's self-help skills and social-emotional competence to increase child-teacher interaction, and suggested that teachers' efforts are needed to help children's social-emotional competence.

Accuracy of FreeStyle Libre flash glucose monitoring in patients with type 2 diabetes who migrated from highlands to plains

BACKGROUND The FreeStyle Libre flash glucose monitoring (FGM) system entered the Chinese market in 2017 to complement the self-monitoring of blood glucose. Due to its increased usage in clinics, the number of studies investigating its accuracy has increased. However, its accuracy has not been investigated in highland popu-lations in China. AIM To evaluate measurements recorded using the FreeStyle Libre FGM system compared with capillary blood glucose measured using the enzyme electrode method in patients with type 2 diabetes (T2D) who had migrated within 3 mo from highlands to plains. METHODS Overall, 68 patients with T2D, selected from those who had recently migrated from highlands to plains (within 3 mo), were hospitalized at the Department of Endocrinology from August to October 2017 and underwent continuous glucose monitoring (CGM) with the FreeStyle Libre FGM system for 14 d. Throughout the study period, fingertip capillary blood glucose was measured daily using the enzyme electrode method (Super GL, China), and blood glucose levels were read from the scanning probe during fasting and 2 h after all three meals. Moreover, the time interval between reading the data from the scanning probe and collecting fingertip capillary blood was controlled to &lt; 5 min. The accuracy of the FGM system was evaluated according to the CGM guidelines. Subsequently, the factors influencing the mean absolute relative difference (MARD) of this system were analyzed by a multiple linear regression method. RESULTS Pearson’s correlation analysis showed that the fingertip and scanned glucose levels were positively correlated (R = 0.86, P = 0.00). The aggregated MARD of scanned glucose was 14.28 ± 13.40%. Parker's error analysis showed that 99.30% of the data pairs were located in areas A and B. According to the probe wear time of the FreeStyle Libre FGM system, MARD1 d and MARD2–14 d were 16.55% and 14.35%, respectively (t = 1.23, P = 0.22). Multiple stepwise regression analysis showed that MARD did not correlate with blood glucose when the largest amplitude of glycemic excursion (LAGE) was &lt; 5.80 mmol/L but negatively correlated with blood glucose when the LAGE was ≥ 5.80 mmol/L. CONCLUSION The FreeStyle Libre FGM system has good accuracy in patients with T2D who had recently migrated from highlands to plains. This system might be ideal for avoiding the effects of high hematocrit on blood glucose monitoring in populations that recently migrated to plains. MARD is mainly influenced by glucose levels and fluctuations, and the accuracy of the system is higher when the blood glucose fluctuation is small. In case of higher blood glucose level fluctuations, deviation in the scanned glucose levels is the highest at extremely low blood glucose levels.

The predictors to self-reported and performance-based physical function in knee osteoarthritis patients: a cross-sectional study.

Osteoarthritis (OA) knee patients have limited ability in physical function, or difficulties with physical tasks and activities may develop disability. This study aimed to observe the predictors of self-reported and performance-based physical function in patients with knee OA by analyzing the impacts of demographic, pathological, and muscle impairment factors. 135 knee OA patients participated in this study to complete self-reported questionnaires using Knee Injury and Osteoarthritis Outcome Score (KOOS). When measuring performance-based physical function, a 6-meter gait speed (6MGS) test was measured to evaluate their mobility, and a 5-time Sit-to-Stand test (5STS) was assessed to evaluate their balance. Pain intensity, knee extensor and flexor muscle strength, age, body mass index (BMI), durations of symptoms, and radiographic severity were also collected. Spearman correlation and stepwise multiple linear regression were used to explore the association and predictors in self-reported and performance-based physical function. BMI and durations of symptoms did not indicate any significant correlation with either self-reported or performance-based physical function. Age is significantly negatively associated with 6MGS (r 2 = -0.383, p < 0.01), while knee extensor muscle strength has a moderate correlation with 5STS (r 2 = -0.528, p < 0.01). In the stepwise multiple linear regression models, pain intensity (β = 0.712, p < 0.001), knee flexor muscle strength (β = 0.112, p = 0.042) were significantly associated with self-reported physical function in daily activities and contributed to 55.0% of the variance in KOOS-PF score. Knee muscle strength, including knee extensor (5STS: β = -0.428, p < 0.001) and flexor muscle strength (6MGS: β = 0.367, p < 0.001), were the main predictors with performance-based physical function. Pain intensity was the leading risk factor of self-reported physical function, and knee flexor muscle strength contributed as well. The severity of knee OA, durations of symptoms and BMI did not contribute to physical function. However, knee extensor and flexor muscle strength were the main predictors of performance-based performance. Our results show that strengthening of weak knee muscles in both quadriceps and hamstring muscle strength should be considered a priory consideration in knee OA no matter if people are in the early or end-stage of knee OA.

Estimating the Total and Regional Body Fat of Physically Active Men Is Not Appropriate for Sedentary Men

Objectives. The reliability of predictive body fat equations remains unclear due to their inappropriate use across different subject cohorts and conditions. The objective of this study was to validate and cross-validate equations to predict total and regional body fat in young physically active males. Material and methods. Three hundred and five young male participants were divided into the following groups: active validation (n=165), active cross-validation (n=70), or sedentary cross-validation ones (n=70). The study used a stratified random sampling based on weekly physical activity level. The total and regional body fat mass were measured using dual-energy X-ray absorptiometry (DEXA) after an overnight fast. Simultaneous measurements of height, body mass, skinfold thickness, body mass index, and body circumferences were taken. The total and regional body fat predictive equations were generated using multiple linear stepwise regression models. The coefficient of determination (R²) and standard error of estimation (SEE) were calculated to examine the accuracy of the predictive equations. Furthermore, cross-validation groups were analysed. Results. The percentage of total body fat, trunk fat, legs fat, arms fat, and body mass index of active cross-validation were found to be significantly lower than in the sedentary cross-validation groups. The total body fat percentage was highly associated with abdominal skinfold thickness (R=0.68-0.74, P&lt;0.001), body mass index (R=0.55, P&lt;0.001), and suprailiac skinfold thickness (R=0.67-0.71, P&lt;0.001) in the active validation group. The predictive total and regional body fat equations of physically active men showed adjusted R² values ranging from 0.35 to 0.66, with standard error of estimation values between 2.74 to 4.35%. The standard error of estimation for the predictive percentage of total and regional body fat in the active cross-validation group was lower than in the sedentary cross-validation group. Conclusions. The findings demonstrate that new predictive total and regional body fat equations can be used to accurately estimate body fat in healthy young active males under fast conditions.

Factors Affecting Depression among First-year Undergraduate Students at A University in Songkhla Province, Thailand

Objective: The purpose of this research was to study factors affecting depression among first-year undergraduate students at a university in Songkhla Province. Theoretical Framework: Psychological factors consist of Self-Esteem (X1), Life satisfaction (X2), Rumination (X3), Problem-solving (X4). Obligation factors consist of Study stress (X5), Taking responsibility for financial obligations (X6). Relationship factors consist of Relationships with friends (X7) and Relationships with family (X8). Methods: Data was collected by surveying 180 students. The research method was a questionnaire consisting of 1) general information about the respondents 2) the Patient Health Questionnaire for Adolescents (PHQ-A) to measure levels of depression, and 3) a questionnaire on factors influencing depression, including psychological factors, obligation factors, and relationship factors. The study identified Cronbach's alpha coefficients, which are equal to 0.76 - 0.93 to analyze the average, standard deviation, Pearson correlation coefficient, and stepwise multiple regression analysis. Results and Discussions: The results found that three factors significantly affect student depression: rumination, self-esteem, and relationship with family. Rumination and self-esteem affect student depression with statistical significance at p&lt;.001 and the relationship with family has a significant effect on student depression at p&lt;.01. These factors can predict 54.70% of the depression of first-year undergraduate students at a university in Songkhla Province (R2 = .547, p &lt; .01). Research Implications: Factors affecting depression among first-year undergraduate students at a university in Songkhla Province included self-esteem, rumination, and relationships with family. Originality/Value: Therefore, the organizations or institutions involved in caring for students should use this as a basis for planning and monitoring depression, paying particular attention to promoting students' self-esteem, management of rumination, and building relationships with families to reduce factors affecting depression for first-year undergraduate students effectively.

Risk Factors and Preventive Measures for Cardiovascular Diseases.

Background: Cardiovascular diseases are the most frequent cause of death worldwide. The aim of this study was to identify and demonstrate correlations between mortality data and etiological factors in EU countries. The relationships presented could thus provide a better understanding of etiological factors and possible points for interventions to prevent cardiovascular diseases. The focus was on the following diseases: hypertensive heart disease, atrial flutter/atrial fibrillation, myocardial infarction, and ischemic heart disease, as well as heart failure. Methods: The data in this study come from WHO databases. Connections between the mortality rates and the possible influencing factors were analyzed. The significant factors from the correlation analysis were simultaneously evaluated using a stepwise multiple regression analysis. Results: Analysis of hypertensive heart disease in women reveals the following factors to be significant: drug expenses, health expenses on gross domestic product, and smoking. For men, population density, first admission to a drug treatment center, and drug expenses per person emerged as important factors. Admission to drug treatment centers and length of hospitalization were significant factors for atrial fibrillation and flutter. Fine dust pollution was the most important factor in heart failure. The most important influencing factor for myocardial infarction and ischemic heart disease is nitrogen dioxide concentration. For women, the second highest value is health expenses, followed by the number of outpatient contacts per year. For men, outpatient contacts are in second place. Conclusions: Prevention measures must be taken by the government. The extent to which population density has an influence on cardiovascular diseases should be examined in more detail. In order to reduce the number of initial admissions to drug treatment centers, behavioral prevention related to drug use could be improved.

Machine learning-based QSAR modeling, molecular docking, dynamics simulation studies for cytotoxicity prediction in MDA-MB231 triple-negative breast cancer cell line

Triple-negative breast cancer (TNBC) is the most aggressive cancer type that tests negative for progesterone, estrogen, and HER2 protein receptors. Therefore, TNBC is unlikely to respond either to drugs that target HER2 receptors or to hormonal therapy drugs. In the present study, 2D-QSAR model was developed to predict the inhibitory concentration (IC50) of terpene derivatives against human breast cancer metastatic cell line MDA-MB231, using V-Life MDS v4.5, module. The model was developed using the forward stepwise multiple linear regression method, with a regression coefficient (r2) of 0.86, and a cross-validated r2 (q2) of 0.8448. Molecular descriptors namely electronegativity (Epsilon-3), carbon atoms separated through five bond distances (T_C_C_5), Sum of Electrotopological state indices of –CH2 group (SssCH2count), Dipole moment of coordinate-z (Zcomp Dipole), and Distance between highest positive and negative electrostatic potential on van-der Waals surface area (Most +ve & -ve Potential Distance) were found to be the most potent contributing chemical descriptors for cytotoxicity against TNBC MDA-MB231 cell line. Additionally, binding affinities and interaction patterns revealed that the proposed compounds exhibit good binding affinities and substantial stability with c-Met-and β-tubulin receptors, as assessed by docking studies. Molecular dynamics simulations (100 ns) and binding free energy calculations were also performed using the MMGBSA method. The pharmacokinetic and eADME/T analysis of predicted compounds were assessed through Discovery Studio software. These findings may be of immense importance in the optimization and development of dual inhibitory potent anti-cancer inhibitors against the MDA-MB231 TNBC cell line. Moreover, this novel QSAR based prediction model/method is implemented in R-package and software tool developed for virtual screening purposes and available online for download through the public repository GitHub.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical s of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Rapid eye movement dependency is associated with increased inflammatory activity in obstructive sleep apnea syndrome

AbstractObjectiveRapid eye movement (REM)‐dependent obstructive sleep apnea syndrome (OSAS) is a specific subtype of OSAS having some phenotypic characteristics like a preference for a younger age, female gender, and milder severity. Such favorable features could make it possible to consider an overall benign course for this phenotype. However, accumulating data introduced its association with several cardiometabolic and vascular disorders recently. The primary objective of this study was to address the disease from the inflammation perspective and evaluate the potential inflammatory status in this variant via two accessible blood parameters: platelet distribution width (PDW) and systemic immune‐inflammation index (SII). The secondary aim was to investigate whether this status, together with other disease characteristics, demonstrates consistency under different definitions of REM‐dependent OSAS published previously.Patients and methodsThe medical records of 35 patients with mild‐to‐moderate REM‐dependent OSAS, 35 age‐ and sex‐matched patients with REM‐independent OSAS, and 25 non‐OSA controls were retrospectively analyzed. Baseline features, polysomnographic characteristics, PDW, and SII were compared between the groups. Secondly, the analyses were repeated using different definitions of REM‐dependent OSAS. Bivariate analyses were performed, and a multiple stepwise regression model was applied to adjust for body mass index (BMI) and cardiovascular risk (CVR) factors. ResultsMean PDW and SII were increased in patients with REM‐dependent OSAS as compared to non‐OSA controls (p = .022 and .029). The significance remained stable after adjustment for BMI and CVRs and was consistent according to different definitions. The Comparison of patients with REM‐independent OSAS and non‐OSA controls, as well as the two different subtypes of OSAS, did not yield significance.ConclusionBased on the current findings, patients with REM‐dependent OSAS appear to be susceptible to inflammation and should be carefully monitored for the negative consequences of that issue. To our knowledge, this study is the first to evaluate SII and PDW in REM‐dependent OSAS.

Risk Management With S&amp;P 500 Stock Price Regression Forecast

For short-term stock market traders who trade with large amounts of capital within a short time frame, having the analytical tool to predict stock price is crucial for the risk management and planning of entry and exit. While retail traders rely on the visualizations of technical indicators to anticipate general price direction and trend lines to estimate areas of concentrated demand or supply, multiple regression modeling uses known quantifiable indices whose functions are based on historical prices of an equity to yield a concise price prediction. This study aims to build an accessible and accurate model for retail traders without advanced software. In order to do that, we must optimize the quality of forecast while minimizing the number of independent variables. Through multiple linear regression and forward stepwise regression, we isolated 3 key variables – the daily closing price, Nasdaq 10-day moving average, and the Dow intraday average – which, when combined again in multiple regression, would produce a model with a multiple-R of 0.9995 and a low standard error of 2.44556268.