Simple Linear Regression Model Research Articles

Long-term effects of COVID-19 on endothelial function, arterial stiffness, and blood pressure in college students: a pre-post-controlled study

BackgroundThe COVID-19 has been shown to have negative effects on the cardiovascular system, but it is unclear how long these effects last in college students. This study aimed to assess the long-term impact of COVID-19 on arterial stiffness, endothelial function, and blood pressure in college students.MethodsWe enrolled 37 college students who had been infected with COVID-19 for more than 2 months. Brachial artery flow-mediated dilation (FMD) was used to assess endothelial function, while arterial stiffness was evaluated using the ABI Systems 100, including variables such as ankle-brachial index (ABI), brachial-ankle pulse wave velocity (baPWV), carotid-femoral pulse wave velocity (cfPWV), heart rate (HR), and blood pressure (BP).ResultsOur results showed that FMD was significantly impaired after COVID-19 infection (p < 0.001), while cfPWV and systolic blood pressure (SBP) were significantly increased (p < 0.05). Simple linear regression models revealed a significant negative correlation between post-COVID-19 measurement time and baPWV change (p < 0.01), indicating an improvement in arterial stiffness over time. However, there was a significant positive correlation between post-COVID-19 measurement time and diastolic blood pressure (DBP) change (p < 0.05), suggesting an increase in BP over time. There were no significant differences in ABI and HR between pre- and post-COVID-19 measurements, and no significant correlations were observed with other variables (p > 0.05). Conclusion: Our study demonstrated that COVID-19 has long-term detrimental effects on vascular function in college students. However, arterial stiffness tends to improve over time, while BP may exhibit the opposite trend.

Health facilities readiness for standard precautions to infection prevention and control in Nepal: A secondary analysis of Nepal Health Facility Survey 2021.

Improvements in standard precaution related to infection prevention and control (IPC) at the national and local-level health facilities (HFs) are critical to ensuring patient's safety, preventing healthcare-associated infections (HAIs), mitigating Antimicrobial Resistance (AMR), protecting health workers, and improving trust in HFs. This study aimed to assess HF's readiness to implement standard precautions for IPC in Nepal. This study conducted a secondary analysis of the nationally-representative Nepal Health Facility Survey (NHFS) 2021 data and used the Service Availability and Readiness Assessment (SARA) Manual from the World Health Organization (WHO) to examine the HF's readiness to implement standard precautions for IPC. The readiness score for IPC was calculated for eight service delivery domains based on the availability of eight tracer items: guidelines for standard precautions, latex gloves, soap and running water or alcohol-based hand rub, single use of standard disposal or auto-disable syringes, disinfectant, safe final disposal of sharps, safe final disposal of infectious wastes, and appropriate storage of infectious waste. We used simple and multiple linear regression and quantile regression models to examine the association of HF's readiness with their characteristics. Results were presented as beta (β) coefficients and 95% confidence interval (95% CI). The overall readiness scores of all HFs, federal/provincial hospitals, local HFs, and private hospitals were 59.9±15.6, 67.1±14.4, 59.6±15.6, and 62.6±15.5, respectively. Across all eight health service delivery domains, the HFs' readiness for tuberculosis services was the lowest (57.8±20.0) and highest for delivery and newborn care services (67.1±15.6). The HFs performing quality assurance activities (β = 3.68; 95%CI: 1.84, 5.51), reviewing clients' opinions (β = 6.66; 95%CI: 2.54, 10.77), and HFs with a monthly meeting (β = 3.28; 95%CI: 1.08, 5.49) had higher readiness scores. The HFs from Bagmati, Gandaki, Lumbini, Karnali and Sudurpaschim had readiness scores higher by 7.80 (95%CI: 5.24, 10.36), 7.73 (95%CI: 4.83, 10.62), 4.76 (95%CI: 2.00, 7.52), 9.40 (95%CI: 6.11, 12.68), and 3.77 (95%CI: 0.81, 6.74) compared to Koshi. The readiness of HFs to implement standard precautions was higher in HFs with quality assurance activities, monthly HF meetings, and mechanisms for reviewing clients' opinions. Emphasizing quality assurance activities, implementing client feedback mechanisms, and promoting effective management practices in HFs with poor readiness can help to enhance IPC efforts.

Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats

BackgroundThis study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).MethodsEighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used.ResultsAt day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035).ConclusionTensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury.Relevance statementTensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI.Key PointsThe alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury.Changing q values were observed in the impact site and remote WM.The lower q values in the remote WM were associated with astrogliosis.Graphical

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

Effects of Government Incentives on Sales Growth of Selected Industrial Firms in South- East, Nigeria

The study focused on the effects of government incentives on sales growth of selected industrial firms in South-East, Nigeria. The objective of the study was to examine the effect of government incentives on sales growth of selected industrial firms in South-East, Nigeria: The significant effect of government incentives on sales growth of selected industrial firms in south east, Nigeria will require management of industrial firms to focus on most lucrative and productive government incentives to improve on their marketing performance. Incentives help firms generate economies of scale in production and they can manage to sell excess domestic capacity with government formulated policies and support with institutional knowledge transfer to facilitate SMEs to industrial development in South East Nigeria. Improved marketing performance of industrial firms in south east, Nigeria will also improve the contribution of industrial firms sector to the gross domestic product of the economy. This will also imply increased tax payment by the firms to government, which will in turn make revenue available for societal development. The study adopted survey research design The population of the study was 12100 selected industrial firms in South-East, Nigeria. A sample size of 387 (Three hundred and eighty-seven) was drawn from the population using Taro yamani statistical formula. One hypothesis was formulated and tested with Simple Linear Regression model.. The major findings in the study showed that: there is a significant effect of government incentive on sales growth of selected industrial firms in South-East, Nigeria; This study has formed a body of knowledge (reference material) which has closed the gap identified in literature and which can also be cited by both present and potential researchers. Results of the study provided direction for management of industrial firms with respect to which government incentive to adopt in order to improve on marketing performance as well as where and how to direct available resources.. Hence, we concluded that successful investments in R&amp;D result in innovative products and services that enable the company to improve its sales revenue. It implies that government subsidies can serve as effective catalyst that can boost the overall productivity of firms located within south east, thereby increasing their sales revenue and also their competitiveness We, therefore, recommended that firms in the south east zone should upgrade their activities relating to cost efficiency, quality, variety/diversification, responsiveness, acceptance of entrepreneurial risks, and a positive attitude towards change and innovation as prerequisites for surviving in a globalized market.

Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.

Student Satisfaction with Lecturer Performance at Pelita Harapan University

The study examined the relationship between student satisfaction and lecturer performance in higher education. This study aimed to identify and measure the relationship between student satisfaction levels and lecturer performance in the academic environment, as well as evaluate the validity and reliability of the research instruments used. This ex post facto research involved students from various majors at UPH Medan campus as the population. Samples were taken randomly using a proportional technique. Data were collected through a Likert scale-based questionnaire and analyzed using inferential statistics. Analysis steps include data grouping, tabulation, and hypothesis testing. Validity, reliability, and classical assumption tests were also applied to ensure the consistency and accuracy of the data. The results showed that all items in the questionnaire were valid, with the highest validity in item number 9 for student satisfaction and number 7 for lecturer performance. The instrument's reliability was also high, with Cronbach's Alpha values of 0.766 for student satisfaction and 0.773 for lecturer performance. The simple linear regression model shows that every one-unit increase in the student satisfaction variable increases lecturer performance by 0.260. The t-test and F-test confirm the significant effect of student satisfaction on lecturer performance, both partially and simultaneously. The Adjusted R Square value of 30.7% indicates that student satisfaction significantly influences lecturer performance. This study confirms that student satisfaction positively and significantly influences lecturer performance. This finding provides important implications for university management to improve lecturer performance through increasing student satisfaction.

The predictive risk factors associated with non-dipper profile in patients with type 2 diabetes and hypertension.

The non-dipper status represented by blood pressure reduction by less than 10 percent during sleep is present in about 50 percent of patients with type 2 diabetes (T2D) and hypertension, a pattern associated with more frequent cardiovascular complications and reserved prognosis. This study analyzed the predictive risk factors associated with the different dipper profiles, especially with the nocturnal pattern, following the mean arterial pressure (MAP), the mean heart rate (MHR), and the mean pulse pressure (MPP) in patients with T2D and hypertension, established by ambulatory blood pressure monitoring (ABPM). 166 consecutive patients with type 2 diabetes mellitus and hypertension were included in a cross-sectional study, and they underwent 24-hour ABPM. We excluded patients with secondary hypertension, acute coronary disease and heart failure, with oncologic or endocrine disease. The simple and multiple linear regression models were performed predicting 24-hour, day and night MAP, MHR, and MPP according to various predictors, using software R version 4.3.1. There were 80 non-dippers (48.20%), 57 dippers (34.34%), 22 reverse-dippers (13.25%) and seven extreme-dippers (4.21%). A statistically significant association was observed between MAP 24-hour and total cholesterol (TC) (higher TC values were associated with higher MAP /24 h values): adjusted coefficient B of the regression slope: 0.09, 95% confidence interval CI (0.04-0.15), p=0.003. In the multivariate analysis: adjusted B: 8.64, 95% CI (-14.67-2.61), p=0.006, beta-blockers reached the threshold of statistical significance in relation to MHR/24 h, their presence decreasing the heart rate. PP/24 hours was associated in the multivariate analysis with age: adjusted B: 0.45, 95% CI (0.05-0.85), p=0.28; abdominal circumference: 0.26, 95% CI (0.03-0.49), p=0.028, and total cholesterol: 0.1, 95% CI (0.02-0.17), p=0.013. Diabetic nephropathy was statistically significantly associated with PP/24 h: adjusted B: 10.19, 95% CI (1.24-19.14), p=0.027. High cholesterol was associated with higher values of MAP and PP. Beta-blocker treatment lowered non-dipper MHR. Age and AC were correlated with increased PP values. These are predictive risk factors associated with the status of non-dippers established by ABPM, and they represent a veritable link to the non-dipper pattern in patients with T2D and hypertension.

Spatial mapping and driving factor Identification for salt-affected soils at continental scale using Machine learning methods

Soil salinization is one of the most serious land-degrading threats globally, particularly in Asia, where there are 21 countries, e.g., Kazakhstan, China, Iran, and Indonesia, lack of accurate spatial information of salt-affected soils currently. Mapping the distribution of salt-affected soils and identifying their main driving factors is critical for sustainable development. Current studies for mapping the spatial distribution of salt-affected soils focused on recognizing the soil salinity by using the indicator of electrical conductivity of saturated soil paste extract (ECe), while cannot distinguish the soil sodicity. Meanwhile, the absence of uncertainty assessment is another problem for large-scale studies. To address these gaps, we developed an effective spatial prediction model that considered both soil salinity and sodicity using machine learning (ML) models and soil salinization data. This study used ECe, pH, and exchangeable sodium percent (ESP) as indicators, with sample sizes of 22,803, 30,821, and 12,961, respectively, to produce salt-affected soil maps with uncertainty assessments in Asia, and conducted the analysis of the drivers of soil salinization. The ML models involved cubist, random forest, quantile regression forest, quantile regression neural network, and a simple multiple linear regression model for comparison. The results showed that among these models, quantile regression forest exhibited the best performance, with mean absolute percentage error values of 2.54 %∼52.06 %, coefficient of determination values of 0.40 ∼ 0.74, and Nash-Sutcliffe efficiency values of 0.36 ∼ 0.71. The predicted maps, with 1 km resolution, demonstrated minimal uncertainty across over 60.4 % of the study area. Approximately 466.36 million hectares of topsoil (0–30 cm) and 377.68 million hectares of subsoil (30–100 cm) in Asia are salt-affected, mainly concentrated in arid, semi-arid, and coastal regions. Climatic factors (the annual precipitation and maximum temperature), topography (digital elevation model and slope), and salinity index V (SI5) were identified as the main drivers of soil salinization. Overall, this study provides important guidelines for soil salinization management and achieving ecological sustainability.

Maize insensitivity to culture filtrates from Exserohilum turcicum is related to quantitative resistance

AbstractTurcicum leaf blight (TLB) of maize is caused by Exserohilum turcicum. The TLB resistance is mainly associated with qualitative race‐specific resistance that is linked to several Ht genes namely Ht1, Ht2, Ht3, HtM, HtN, HtNB and HtP. However, quantitative TLB resistance also occurs, but its mechanisms are poorly understood. In this study, tolerance or insensitivity to chlorosis and necrosis of E. turcicum culture filtrate was associated with quantitative TLB resistance. A novel detached maize seedling assay was developed for E. turcicum culture filtrate using methanol‐treated modified Fries medium. Screening of E. turcicum isolates of races 2, 3, 23, 3 N, 23 N and 123 N against 61 maize inbred lines with diverse levels of resistance to TLB was conducted in the greenhouse by fungal inoculation and the detached seedling culture filtrate assay. Using an isolate of race 123 N, which can overcome all the qualitative resistance genes, a simple linear regression model (R2 = 0.68, p = .90) for the 61 inbred lines was established between disease severity from foliar inoculations and culture filtrate symptom rating of the detached seedling assay. Two maize lines CML474 and CML483 had relatively high culture filtrate symptom ratings but very low disease severity with foliar inoculation indicating that these sources of quantitative TLB resistance may be unrelated to tolerance to E. turcicum culture filtrates, whose effect on seedlings could be due to the toxin known as HT‐toxin or monocerin.

ECONOMIC GROWTH AND POVERTY: THE MODERATING EFFECT OF INCOME INEQUALITY

The trickle-down effect assumes that the progress of a group of people will automatically trickle down, thus creating jobs which in turn will promote the necessary conditions for equitable economic growth. However, previous studies on the association between economic growth and poverty have shown inconclusive results.This study fills the gap of the above research differences by introducing income inequality as a moderating variable in the relationship between economic growth and poverty. Secondary data from 34 provinces in Indonesia during 2016-2020 period were analyzed using panel data moderated regression. The results showed that while simple linear regression model indicates positive significant effect of economic growth on poverty, the interaction between economic growth and income inequality shows negative significant effect on poverty. This paper concludes that income inequality as measured by the Gini index moderates the effect of economic growth on poverty which supports the importance of inclusive economic growth.

Strengthening Muamalah Maliyah's Literacy Understanding of Online Transactions in Indonesia

This research aims to test and analyze the understanding of students majoring in Commerce Management at the ALKS study program in implementing online buying and selling schemes from an Islamic perspective, namely by using the Muamalah Maaliyaah contract. The population in this study was 278 people from each generation of the ALKS study program, a sample of at least 10 people was taken and then the sample size became 100 people. This research uses a quantitative approach. The data collection technique used was by distributing questionnaires and interviews. The model used is a simple linear regression model with statistical analysis tests used are the classical assumption test, T-test, validity, and reliability tests. The research results show that from a simple regression test, the results of the t-test variable for the variable Understanding the Muamalah Maaliyaah Agreement show t_count of 15.024 &gt; t_tabel of 1.98472 and a Sig value of 0.000 &lt; 0.05. These results show that "There is a significant relationship between the influence of Understanding the Muamalah Maaliyah Agreement (X) on the decision to carry out online transactions (Y)". It can be concluded that there is a very large influence of ALKS study program students' muamalah literacy on the decision to carry out online transactions.

Effects of multicollinearity and data granularity on regression models of stream temperature

Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability.

An exercise and patient education intervention to reduce pain and physical limitations in adults with acetabular dysplasia: study protocol for a process evaluation integrated within a randomised controlled trial (the MovetheHip trial)

BackgroundThe Movethehip trial investigates the effectiveness of an exercise and patient education intervention for adults with acetabular dysplasia. The intervention involves eight tailored one-to-one sessions with trained providers who employ supportive feedback tools. The present protocol reports a planned process evaluation, which aims to determine how the intervention functions by examining the implementation of the intervention (process, dose and reach), its acceptability, mechanisms of change and the influence of contextual factors.MethodsTwo hundred trial participants aged 18–50 years will be recruited from a University Hospital in Denmark and randomised to the intervention or control group. Approximately ten providers will deliver the intervention. The process evaluation adopts a concurrent mixed-methods design. The implementation will be assessed using self-report questionnaires (at baseline and 6-month follow-up), training records and semi-structured focus group interviews with intervention providers (n = 10) and healthcare managers (n = 4–6). The mechanisms of change will be explored through semi-structured one-to-one interviews (at baseline and 6-month follow-up) with 15–20 purposefully sampled participants and by measuring changes in health outcomes (self-reported pain, physical functioning and quality of life completed at baseline and at 3- and 6-month follow-up). Additionally, change will be measured through an explorative examination of associations between dose and change in health outcomes, applying simple linear regression models. The acceptability of the intervention and the influence of contextual factors will be explored through one-to-one participant interviews and focus group interviews with 4–6 healthcare managers. The interviews will focus on expectations, experiences, events, personal understandings and interaction with interpersonal and organisational aspects. Interview data will be analysed using theoretical thematic analyses, and findings will be merged with quantitative data and reported jointly on a theme-by-theme basis.DiscussionThe process evaluation conducted as part of the MovetheHip trial will illuminate how the intervention functions, and if the intervention is proven effective, the findings of the evaluation will contribute to pinpoint how the intervention may be optimised to facilitate future up-scaling and implementation.Trial registrationThe MovetheHip protocol was approved by the Committee on Health Research Ethics in the Central Denmark Region. ClinicalTrials, NCT04795843. Registered on 20 March 2021.

Predicting monthly gold prices in indian rupees using ARIMA, LSTM, GRU, and Simple Linear Regression models

Predicting monthly gold prices in indian rupees using ARIMA, LSTM, GRU, and Simple Linear Regression models

Three-dimensional prediction of the nose for facial reconstruction: A preliminary study on North Indian adults

Forensic facial reconstruction is the last recourse to establish the identity of an unknown skull. The facial soft-tissue thickness (FSTT) is required to reconstruct various facial features on a skull. Unlike other facial features, the nose is made of cartilaginous tissue except for a small nasal bone. A large cavity (pyriform aperture) exists on the skull in place of the nose, which makes it a challenging job for reconstruction. The nose is a vital feature for the recognition of a face. Any change in the shape or size of the nose can alter the original aesthetic of the face. The present study proposes angles and regression functions on the bony structure to predict the various parts of the soft nose. A sample of computed tomography (CT) images of 100 males and 100 females aged between 18 and 45 years were included in the study. Apart from measuring fourteen linear parameters with three angles, simple linear regression models were derived for five pairs of parameters. Pearson's correlation coefficients for most of the parameters ranging between 0.221 and 0.872 were found to be significant at p ≤ 0.05 level. FSTT at three anatomical landmarks of the nose was also measured. A morphological observation study was undertaken to find the most frequent direction of the bony anterior nasal spine (ans) and its relation with the position of the pronasale (prn) on the soft nose. The devised parameters proposed in the study may also prove useful for reconstructing the nose in other populations.

Accounting for center-level effects in multicenter randomized controlled trials

Investigators often conduct randomized controlled trials (RCTs) at multiple centers/sites when determining the effect of a treatment or an intervention. Diversifying recruitment across multiple institutions allows investigators to make recruitment go faster within a shorter timeframe and allows generalizing the study results across diverse populations. Despite having a common study protocol across multiple centers, the eligible participants may be heterogeneous, site policies and practices may vary, and the investigators’ experience, training, and expertise may also vary across sites. These factors may contribute to the heterogeneity in effect estimates across centers. As a result, we usually observe some degree of heterogeneity in effect estimates across centers, despite all centers following the same study protocol. During the analysis of such a trial, investigators typically ignore center effects, but some have suggested considering centers as fixed or random effects in the model. It is not clear how considering the effects of centers, either as fixed or random effects, impacts the test of the primary hypothesis. In this article, we first review the practice of accounting for center effects in the analyses of published RCTs and illustrate the extent of heterogeneity observed in a few preexisting multicenter RCTs. To determine the impact of heterogeneity on the test of a primary hypothesis of an RCT, we considered continuous and binary outcomes and the corresponding appropriate model, namely, a simple linear regression model for a continuous outcome and a logistic regression model for the binary outcome. For each model type, we considered three methods: (a) ignore the center effect, (b) account for centers as fixed effects, or (c) account for centers as random effects. Based on simulation studies of these models, we then examine whether considering the center as a fixed or random effect in the model helps to preserve or reduce the type I and type II error rates during the analysis phase of an RCT. Finally, we outline the threshold at which center-level effects are negligible and thus negligible and provide recommendations on when it may be necessary to account for center effects during the analyses of multicenter randomized controlled trials.

Weight Development and Growth Curves of Grazing Santa Inês Sheep Supplemented with Concentrate in the Pre-Weaning Phase.

Monitoring weight development is essential for decision-making and assessing the effectiveness of management strategies. However, this practice is often hindered by the lack of scales on farms. This study aimed to characterize the weight development and growth curves of male and female Santa Inês lambs from birth to weaning, managed on pasture with creep-fed concentrate supplementation. Data from 212 lambs during the pre-weaning phase were analyzed. The animals were weighed every seven days to evaluate total weight gain and average daily gain. Biometric measurements were taken every 28 days. Mixed models were used to assess the effects of sex and birth type on birth and weaning weights. Simple and multiple linear regression models were employed to estimate live weight using biometric measurements. The non-linear Gompertz model was utilized to describe weight development and formulate growth curves. Results were considered significant at p < 0.05. An interaction effect between birth type and sex (p < 0.05) was noted for birth weight, with the lowest weight observed in twin-birth females (2.96 kg) and the highest in single-birth males (3.73 kg) and females (3.65 kg) (p > 0.05). Birth type significantly influenced average daily gain, total weight gain, and weaning weight (p < 0.05). The Gompertz model accurately depicted the growth curves, effectively describing the weight development. Pearson's correlation coefficients between biometric measurements and weight were positive and significant (p < 0.05), ranging from 0.599 for hip height to 0.847 for heart girth. Consequently, the simple and multiple regression equations demonstrated high precision in predicting weaning weight. In conclusion, twin-birth lambs receiving concentrate supplementation via creep-feeding and managed on pasture showed different developmental patterns compared to single-birth lambs under the same conditions. The Gompertz model proved effective for monitoring development during the pre-weaning phase. All simple and multiple linear regression models were effective in predicting weaning weight through biometric measurements. However, for practical application, the model incorporating two measurements-body length and abdominal circumference-is recommended.

Prevalence of musculoskeletal pain and associated factors among professional orchestra musicians in Norway.

Orchestra musicians have a high risk of experiencing musculoskeletal problems. These problems may lead to sleep and psychological disturbance. This study investigated the occurrence of musculoskeletal pain (MSP) among orchestra musicians and the coherence between pain and predictors such as gender, age and instrument. Further, the impact of pain on sleep and mental health was analysed to assess pain as a predictor threatening musicians' wellbeing. Some 358 musicians in eight different professional orchestras in Norway completed a questionnaire. Questions about pain experienced within the last 30 days were based on a national survey. Standardised screening tools like the Hopkins Symptom Checklist-25 (HSCL-25) and Bergen Insomnia Scale (BIS) were used to assess psychological distress and sleeping problems. Kruskal-Wallis tests were used to analyse the differences in the pain-related variables: experienced pain severity (EPS), number of pain areas, and work being perceived as the source of pain based on age. Mann-Whitney U tests were conducted to evaluate the statistical significance between gender and the pain-related variables. Spearman's rank correlations were used to examine the relationship between EPS and BIS, HSCL-25 and pain area. Additionally, simple linear regression models were employed to determine whether EPS can predict higher scores on the HSCL-25 and BIS scales. The prevalence of MSP experienced in the last 30 days among orchestra musicians in Norway was 85%. Musicians experienced frequent pain regardless of their gender, age or instrument group. Neck, shoulders and upper back represented the most reported pain areas. Further, it was observed that increased EPS was correlated with an increase in the number of pain areas and in work being perceived as the source of pain. EPS significantly predicted sleep- and psychological distress. Orchestra musicians have a high risk of encountering MSP. Furthermore, their psychological distress and sleeping problems seem to be related to experienced MSP. To deliver optimal health, these problems should be handled simultaneously.

Breakfast consumption, saturated fat intake, and body mass index among medical and non-medical students: a cross-sectional analysis

Changes in dietary patterns and body weight have become a focus of research in undergraduate students. This study compared breakfast consumption, intake of foods high in saturated fat, and BMI between medical and non-medical students. A comparative cross-sectional study was conducted in 4,561 Peruvian university students, of whom 1,464 (32.1%) were from the medical field and 3,097 (67.9%) from the non-medical field. We compared the frequency of breakfast consumption (categorized as regular: 6 to 7 days/week; occasional: 3 to 5 days/week; and rarely or never: 0 to 2 days/week) and the frequency of consumption of foods high in saturated fat. We created simple and multiple linear and Poisson regression models with robust variance to evaluate the association of the mentioned variables with academic fields. Non-medical students (Adjusted Prevalence Ratio [PR] = 0.92, 95% CI 0.86–0.99; p = 0.008) were less likely to eat breakfast regularly compared to medical students. Likewise, consumption of foods high in saturated fats was higher in non-medical students (B = 1.47, 95% CI 0.91–2.04; p < 0.001) compared to medical students. Similarly, the mean BMI of these students was significantly higher than that of medical students (B = 0.33, 95% CI 0.12–0.53; p = 0.002). Although medical students reported relatively healthy eating habits and a lower BMI, there is a widespread need to promote improved diet and lifestyle among the entire university population to reduce the risks of communicable diseases and improve quality of life.