Multiple Linear Regression Models Research Articles

Effects of a sequential square mat walking training program Fisior® on improving physical performance in older adults with Parkinson's disease: A pilot study

BackgroundThis study aimed to assess the efficacy of a 12-week functional walking training program utilizing a sequential square mat (Fisior® Mat) on the physical performance of older adults diagnosed with Parkinson's Disease (PD). MethodsParticipants, aged over 60 and diagnosed with mild to moderate PD, underwent progressive resistance training sessions lasting 30-40 minutes, three times a week, using the Fisior® sequential square mat. A randomized clinical trial was conducted with participants from a patient association. Multiple linear regression models were employed to analyze physical performance variation between intervention and control groups. ResultsTwenty-eight patients (14 in each group) participated, with improvements observed in total Short Physical Performance Battery (SPPB) score, chair test score, gait speed score, and balance test score. Participants in the intervention group showed significant improvement in chair and balance test scores compared to controls. ConclusionThe Fisior® sequential square mat program, as an adjunct to conventional outpatient physiotherapy, effectively enhances physical performance in older adults with PD.

Research on the Factors Affecting House Price using Multiple Linear Regression Model

This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.

Application of Multiple Linear Regression Models in Predicting the Value of Football Players

In this study, the key factors affecting football player value were analyzed using a multiple linear regression model. The analysis considered variables such as nationality, location, historical achievements, experience, skill level, and market value. The results indicate that a player’s competitive level significantly enhances their value, while there is a negative correlation between market value and player value. The relationship between experience and value is complex and requires further exploration. Additionally, the study examined the distribution of players from various countries in the English Premier League, with French, Spanish, and Brazilian players ranking highest in appearances. French and Spanish players are versatile across positions, while Brazilian players excel primarily as forwards. Visual analyses demonstrated the proportions and performances of players in different positions, confirming the positive correlation between competitive level and net worth and the negative correlation with market value. The intricate relationship between experience and net worth was also highlighted. These findings offer valuable insights into the determinants of football player value and provide data to support team management and future research.

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.

Predictors of ventricular and atrial feature tracking-derived global longitudinal strain in healthy athletes and volunteers

Abstract Background Feature tracking (FT) strain analysis provides insights into the mechanics of the heart and carries essential prognostic and diagnostic information without prolonging image acquisition during cardiac magnetic resonance (CMR) scans. However, data regarding CMR-derived FT strain in athletes is scarce, especially in the atria. We explored the determinants of FT global longitudinal strain (GLS) values concerning all four cardiac chambers in healthy competitive athletes and less active volunteers. Methods Long- and short-axis cine sequences were analyzed with Medis Suite v4.0 (Leiden, The Netherlands) to assess FT strain. After excluding individuals with preexisting pathologies and images with quality issues, subjects were classified by sport types following the 2020 ESC Sports Cardiology Guidelines. Multiple linear regression models were constructed to assess the impact of age, sex (male=0, female=1), weekly training hours, and sports category (power coded as 0, mixed as 1, endurance as 2) on GLS values. Atrial strain values are positive, and ventricular ones are negative. Intercepts/constants are denoted as "baseline" for easier comprehension. Results Native CMR scans acquired at a tertiary cardiac centre of 596 healthy Caucasian subjects (411 athletes: 145 females, mean age 21±6, range 14-36 years; and 185 volunteers: 86 females, mean age 24±5, range 13-35 years) were included. Athletes had a high exercise load versus the controls who performed no or maximum recreational physical activity (19±7 vs. 3±2 hours/week, p<.001). When assessing athletes (weekly training≥6h), RA GLS (baseline: 34%, p<.001) is increased in females (β=2.0) but is expected to decrease with every one-hour increase in training hours (β=-0.2). LA GLS is notably affected by sex alone (baseline: 37%, β=3.6, p<.001), suggesting markedly higher values for females. RV GLS (baseline: -30%, p<.001) is associated with both age and training hours (β=0.1 for each), making it less negative. Conversely, LV GLS (baseline: -25%, p<.0001) is significantly modulated by all studied factors, with sports category (β=0.5), age, and training hours (β=0.1 each) increasing values and sex decreasing them (β=-0.9). In less active volunteers (weekly training<6h), sex (β=4.7) is the only significant factor for RA GLS (baseline: 33%, p=.04), and sports category (β=2.7, the group included 98 recreational athletes with low training loads) for LA GLS (baseline: 38%, p=.04). No variables were retained in the model for RV GLS, but sex (β=-1.4) remained a predictor in the LV GLS model (baseline: -21%, p=.002). Conclusion GLS shows significant variations influenced by age, sex, sports category, and training hours across all four heart chambers in athletes. These effects appear mostly inconclusive among less active individuals. This data calls for further advanced analyses of athlete's hearts to refine differentiation methods between healthy adaptation and cardiac pathologies.Left atrial FT analysisModel outputs for GLS predictions

The relationship between anxiety and diet quality in adolescent populations: a cross-sectional analysis.

Globally, more than 13 % of adolescents have clinically significant mental health problems, with anxiety and depression comprising over 40 % of cases. Despite the high prevalence of anxiety disorders among youth, dietary research has been focused on youth with depression, resulting in a significant knowledge gap regarding the impact of anxiety on adolescent diet quality. Adolescents with diagnosed anxiety disorders and healthy controls were included in this study. Anxiety symptoms were measured using the Screen for Child Anxiety-Related Disorders. Diagnosis of anxiety disorder was determined using the Kiddie Schedule for Affective Disorders and Schizophrenia interview. Five diet quality indices were scored from FFQ. Diet quality indices associated with anxiety symptoms in the correlation matrix were interrogated using multiple linear regression modelling. All models were adjusted for depression. One hundred and twenty-eight adolescents (mean age 14·8 years (sd: 2·1); 66·4 % female) were included in this cross-sectional analysis. Although healthy controls and outpatient participants had similar unhealthy dietary index subscale scores, outpatient participants had lower healthy index scores. Higher anxiety symptoms were associated with lower healthy dietary indices in univariate analysis; after adjusting for comorbid depression; however, anxiety symptoms were no longer associated with dietary indices following adjustment for multiple testing (P = 0·038 to P = 0·077). The association between anxiety symptoms and a poor diet is attenuated by depression. The results of this study support the need for an integrated approach to the assessment of mental and physical well-being and further research aimed at understanding the unique contribution of depression to healthy dietary patterns.

Ethnic minorities who complete cardiac rehabilitation achieve equivalent functional capacity outcomes after cardiac rehabilitation but only half complete

Abstract Background Cardiac rehabilitation (CR) reduces the risk of death and repeat cardiac events and improves multiple patient outcomes. Ethnic minority backgrounds experience significant health disparities, often due to lack of fluency in dominant language and higher rates of cardiovascular disease risk factors. Despite these issues, outcomes for ethnic minorities from CR are rarely differentiated and participation in CR programs infrequently reported. Purpose To compare participation in and exercise outcomes from CR programs for patients from ethnic minorities versus the majority. Methods A secondary analysis from a national CR audit including 2335 CR participants. Ethnic minority was defined as having poor English language fluency requiring an interpreter for CR assessment. Functional capacity was assessed using 6-minute walk test (6MWT) or metabolic equivalent of tasks (METs). Binary logistic regression model was created to identify independent predictors of completion, accounting for baseline variables. Multiple linear regression models were used to determine if ethnic minority status was an independent predictor of change in 6MWT and METs after adjusting for baseline characteristics. Results Of the 2335 participants, 326 (14%) were from ethnic minorities. The mean age was 66.3±12.4 years and 70% males. There were no differences between minority and majority groups in relation to baseline characteristics. The most common referral diagnosis was myocardial infarction (27%), angina (25%), other – arrhythmias, cardiac devices, valve diseases/procedures (24%), and cardiac surgery (24%). Ethnic minorities were half as likely to complete CR (OR 0.49, 95% CI 0.38, 0.65) adjusted for age, sex, diagnosis, minority status, smoking, referral site and wait time (Table 1). Other factors that increase completion likelihood included all other cardiac diagnoses (compared to myocardial infarction as reference) and non-smokers, whereas major referral centre and female sex negatively impacted completion. Patients from ethnic minority backgrounds had less functional capacity at CR entry compared to the majority (379.9±118 versus 430.7±103 metres, p<0.001), though METs were comparable (6.69±2.73 versus 6.46±2.28 mg/kg/min, p=0.69). However, improvements were equivalent for 6MWT: minority 71.03±58.57 versus majority 71.37±61.16 (p=0.96), and for METs: minority 3.01±1.66 versus majority 3.13±1.74 mg/kg/min (p=0.77). Ethnic minority status was not an independent predictor of change in 6MWT and METs after accounting for age, sex, diagnosis, smoking, referral site, and wait time. Conclusion For patients from ethnic minority groups who completed CR, exercise/functional capacity outcomes were equivalent to majority participants, however less than one-in-two completed. CR programs should consider the needs of ethnic minority groups, particularly in relation to language and tailor content and delivery to promote participation and ensure more equitable access.

Association of physical activity with health-related quality of life in rectal cancer survivors

Abstract Background Health-related quality of life (HRQoL) in rectal cancer survivors after completion of surgical treatment may be influenced by various factors including physical activity. The purpose of this study was to examine the association between regular exercise and physical and mental health in rectal cancer survivors. Methods People who underwent surgical resection of rectal cancer with temporary ileostomy from 2018 to 2021 were invited to participate. Participants were drawn from the Clinic for Digestive Surgery and the Clinic for Emergency Surgery of the University Clinical Center of Serbia, as well as the nationwide laparoscopic program - LapSerb consortium. Data were collected through interviews in late 2022 and early 2023. The HRQoL was evaluated using the physical composite score (PCS) and mental composite score (MCS) of the Short Form-36 (SF-36) questionnaire. Socio-demographic, clinical and behavioral characteristics including the frequency of exercise were analyzed in multiple linear regression models. Results A total of 134 rectal cancer survivors were tested. Few participants (18.7%) exercised regularly, while most of the study population occasionally (41.8%) or never engaged (39.5%) in physical activity. Rectal cancer survivors who exercised regularly had significantly higher scores of physical (75.5 vs. 62.8, p = 0.019) and mental quality of life (78.9 vs. 63.8, p = 0.008). Regular exercise remained associated with better physical and mental health after the adjustment for demographic, clinical and behavioral characteristics. Conclusions Consistent physical exercise seems to be significant contributor of better physical and mental quality of life in rectal cancer survivors after completion of surgical treatment. Key messages • Physical exercise may help rectal cancer survivors improve their physical and mental health. • Promotion of physcial activity in rectal cancer survivors could optimize recovery after surgery.

Association between GDF-15 levels and cardiac morphology and function in cardiac magnetic resonance imaging: insights from UK Biobank data analysis

Abstract Background/Introduction Growth Differentiation Factor-15 (GDF-15) has emerged as a biomarker associated with cardiovascular disease and adverse cardiac outcomes. Cardiac morphology and function, assessed through cardiac magnetic resonance imaging (CMR), are critical indicators of cardiovascular pathology. Understanding the relationship between GDF-15 levels and LV characteristics could provide valuable insights into cardiac pathophysiology and disease progression. Purpose This study aims to investigate the association between GDF-15 levels and cardiac morphology and function, utilizing data from the UK Biobank and CMR. By analyzing various CMR-derived parameters, including LV strain, myocardial mass, LV mass-to-volume ratio, ventricular and atrial volumes, we seek to elucidate the impact of GDF-15 on cardiac structure and function. Methods Data from the UK Biobank, including 2,264 participants, were analyzed. The median age of the analyzed participants was 55 years, with a median BMI of 26.0. Among the participants, 53% were females and 47% were males. CMR-derived variables related to cardiac morphology and function, along with GDF-15 levels, were examined. Multiple linear regression models were constructed to assess the association between GDF-15 levels and cardiac parameters, adjusting for age, sex, and other relevant covariates. Confidence intervals (CI) and p-values were calculated to determine the significance of observed associations. Results Analysis of the UK Biobank data revealed significant associations between GDF-15 levels and specific cardiac parameters (table1). Notably, GDF-15 levels were negatively associated with LV myocardial mass (LVM)(coefficient = -1.146, p = 0.0071), LV end-diastolic volume LVEDV)(coefficient = -3.321, p < 0.0001), LV end-systolic volume (coefficient = -1.876, p < 0.0001), as well as right atrial maximum volume (coefficient = -3.025, p < 0.0001) and minimum volume (coefficient = -1.858, p = 0.0003). In addition, LVM/LVEDV was positively associated with GDF-15 level (p<0.03). However, no significant associations were observed between GDF-15 levels and LV strain parameters, ejection fraction, or left atrial volumes. Conclusion This study provides evidence of a significant association between GDF-15 levels and selected parameters of cardiac morphology and function, as assessed by CMR in the UK Biobank population. These findings suggest a potential role of GDF-15 in modulating specific aspects of cardiac structure, function and left ventricle concentric remodeling warranting further investigation.GDF 15

The pivotal role of positive emotions in maintaining nurses’ trust and preventing pandemic fatigue

Abstract Background Nursing is a challenging profession that demands quick adaptation to changing conditions. The COVID-19 pandemic has placed unprecedented pressure on nursing teams, leading to high-stress levels and uncertainty. This study examines how nurses’ emotional response at the start of the pandemic predicts their trust in the healthcare system and pandemic fatigue after three morbidity waves. Methods This study included collecting self-reported data among Israeli nurses in three time periods: 346 nurses during March-May 2020 (T1), 260 nurses during June-July 2020 (T2), and 91 nurses during February 2022 (T3). Change in the study variables was assessed using repeated-measure ANOVA. Two multiple linear regression models were calculated for trust and pandemic fatigue at T3, with the emotions at T1 and professional seniority as predictors. Results A significant increase in negative emotions was observed between T1 and T2 (M = 2.65, SD = 1.12 and M = 3.20, SD = 1.29 respectively). Furthermore, there was a significant decrease in trust levels between T1 and T3 (M = 4.68, SD = 1.14 and M = 4.07, SD = 1.08 respectively). The regression model for trust was significant, with 20% of the variance explained in it, revealing that higher positive emotions at T1 predicted higher trust at T3. Similarly, the regression model for pandemic fatigue was significant (R2=22%), showing that lower professional seniority and lower positive emotions at T1 were predictors of higher pandemic fatigue at T3. Conclusions During a crisis, nurses may experience intense negative emotions like fear and anxiety. However, positive emotions, such as pride and a sense of mission, are crucial in maintaining their trust and preventing the onset of pandemic fatigue. Healthcare organizations should promote emotional support and provide emotional resources so that nurses can continue to deliver high-quality care even during challenging times. Key messages • During times of crisis, it’s crucial to foster positivity and encourage a sense of pride and accomplishment in nursing staff. • Healthcare organizations must provide emotional support to nursing teams in routine and crisis situations to prevent burnout and maintain trust and long-term functioning.

Impact of HHIP gene polymorphisms on phenotypes, serum IL-17 and IL-18 in COPD patients of the Chinese Han population

BackgroundGenetic factors, including the Hedgehog Interacting Protein (HHIP) gene, play a crucial role in Chronic Obstructive Pulmonary Disease (COPD) susceptibility. This study examines the association between HHIP gene polymorphisms and COPD susceptibility, phenotypes, and serum IL-17 and IL-18 levels in a Han Chinese population.MethodsA case-control study was conducted with 300 COPD patients and 300 healthy controls in Chinese Han population. Participants underwent genotyping for HHIP gene polymorphisms, pulmonary function tests, and quantitative CT scans. DNA samples were sequenced using a custom chip targeting the HHIP gene. Serum IL-17 and IL-18 levels were measured by enzyme-linked immunosorbent assay. Associations between SNPs, COPD susceptibility, and phenotypes were analyzed using logistic and multiple linear regression models, adjusting for confounders.ResultsOur study identified the rs11100865 polymorphism in the HHIP gene as significantly associated with COPD susceptibility (OR 2.479, 95% CI 1.527–4.024, P = 2.39E-04) after screening 114 SNPs through rigorous quality control. Stratified analyses further indicated this association was particularly in individuals aged 60 or older. Serum levels of IL-17 and IL-18 were significantly elevated in COPD patients compared to controls, with rs11100865 showing a notable association with IL-18 levels (B = 49.654, SE = 19.627, P = 0.012). However, no significant associations were observed between rs11100865 and serum IL-17 levels, COPD-related imaging parameters, or clinical phenotypes.ConclusionThis study identified a significant association between HHIP gene polymorphisms and COPD susceptibility in a Han Chinese population, with connections to inflammation, but found no significant associations between this SNP and COPD-related imaging or clinical phenotypes.Trial registrationwww.chictr.org.cn ID: ChiCTR2300071579 2023-05-18.

Lipoprotein(a) levels and cholesterol efflux capacity: inverse association and impact on atherosclerosis in familial hypercholesterolemia patients on lipid-lowering therapy

Abstract Backgrounds Familial hypercholesterolemia (FH) patients have higher serum lipoprotein(a) (Lp(a)) levels than non-FH patients, and high Lp(a) levels raise atherosclerotic cardiovascular disease (ASCVD) risk 1). We previously reported that decreased cholesterol efflux capacity (CEC) is a risk marker for ASCVD in patients with statin-treated FH 2). Others have reported that Lp(a) interferes with the enhancement of ABCA1-mediated cholesterol efflux by plasminogen and that ABCA1-mediated CEC is lower in patients with Lp(a) >50 mg/dL. However, the association of CEC, not necessarily ABCA1-mediated, with Lp(a) is not clear and has not been studied in patients with FH. It is also unclear whether high Lp(a) and low CEC in patients with FH are associated with the severity of atherosclerosis. Purpose We examined whether Lp(a) levels are negatively associated with ABCA1-independent CEC in patients with FH receiving lipid-lowering therapy. We also examined the impact of the combination of high Lp(a) and low CEC on the presence of corneal arcus, Achilles tendon thickness, carotid intima-media thickness (IMT), and pre-existing ASCVD. Methods This cross-sectional study included 371 patients clinically diagnosed with FH receiving lipid-lowering therapy. CEC was measured in apolipoprotein B-depleted plasma and 3H-cholesterol-labeled J774.1 cells without cAMP-stimulated ABCA1 overexpression. Lp(a) was measured by enzymatic methods (Sekisui Medical, Tokyo, Japan) using an automated analyser. Achilles tendon thickness was measured by X-ray and carotid IMT by ultrasound. Results Mean age was 55±16 years, and 191 (51%) were women. The median serum Lp(a) level was 18.7 mg/dL, higher than the previously reported median level (13 mg/dL) in a Japanese population. One hundred nineteen patients (32%) had serum Lp(a) levels of 30 mg/dL or higher, and they had significantly lower CEC (P=0.0015). This negative association persisted after adjusting for age, sex, BMI, smoking status, serum LDL-cholesterol, serum triglycerides, and eGFR in a multiple linear regression model (beta-coefficient: -0.047, 95% CI: -0.075 - -0.019, P=0.0011). In addition, patients with both Lp(a) > 30 mg/dL and CEC lower than the median had a higher percentage of corneal arcus, thicker Achilles tendons, greater carotid IMT, and a higher prevalence of pre-existing ASCVD compared to patients with either or neither. Conclusions We found an inverse relationship between Lp(a) levels and CEC in FH patients receiving lipid-lowering therapy. Our results suggest that Lp(a) may also inhibit ABCA1-independent CEC, including passive cholesterol diffusion from peripheral cells. Furthermore, the combination of high Lp(a) and low CEC may be a residual risk for patients with FH receiving lipid-lowering therapy. Future studies are needed to determine whether agents that decrease Lp(a) levels will increase CEC and thereby resolve part of the residual ASCVD risk in FH.

Age and sex differences in the effects of short- and long-term exposure to air pollution on endothelial dysfunction

Abstract Background Endothelial dysfunction is an integrated index of risk factor burden and a marker of atherosclerotic cardiovascular disease. Air pollution is widely acknowledged as a significant non-traditional risk factor for cardiovascular disease. However, the impact of air pollutants at varying concentrations and exposure durations on endothelial function in different populations remains unclear. Purpose We aimed to use brachial artery flow-mediated dilatation (FMD) to identify demographic differences in the effects of short- and long-term exposure to air pollution on endothelial dysfunction. Methods We measured FMD in 850 participants from October 2016 to January 2020. Location-specific concentrations of fine particulate matter <2.5 μm aerodynamic diameter (PM2.5), inhalable particulate matter <10 μm aerodynamic diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) measured by fixed ambient air monitoring stations were collected for short- and long-term exposure assessment. We used the moving average 7- and 14-day exposures before the date of FMD measurements to estimate the short-term exposure effects and the moving average 12-month exposures before the date of FMD measurements to estimate the long-term exposure effects. Multiple linear regression models and restricted cubic splines were used to assess the associations before and after stratified by age and sex. Results This study eventually included 828 participants [551 (66.5%) younger than 65 years and 553 (66.8%) men]. Each 10 μg/m³ increase in 12-month exposure to PM10 and NO2 was significantly and linearly associated with -0.20% (95% CI: -0.37 to -0.03) and -0.58% (95% CI: -0.99 to -0.18) changes in FMD in all participants. 12-month exposure to gaseous pollutants was also significantly but nonlinearly associated with lower FMD. 7-day exposure to Quartile 4 of SO2 was significantly associated with a 0.70% decrease in FMD compared to Quartile 1 (β = -0.70, 95% CI: -1.40 to -0.008). No relationships were observed between 7- and 14-day exposure to PM2.5, PM10, NO2, CO, and endothelial dysfunction. Although interaction effects were limited to SO2 and sex, significant associations with different air pollutants were only found in men and those younger than 65 years after stratified. Conclusions Long-term exposure to air pollution is strongly associated with decreased endothelial function, and susceptibility to air pollution varies significantly with age and sex. Further identification of different susceptible populations and proactive measures to mitigate air pollution may be beneficial in reducing the burden of cardiovascular disease.

Emergency Department admissions for Acute Respiratory Infections: a retrospective analysis

Abstract Background Acute Respiratory Infections (ARIs) have a relevant impact on public health in terms of prevalence and costs associated with the diseases. The COVID-19 pandemic highlighted the need to adopt accurate surveillance systems to respond to new emergencies and meet the demand for access to care. The aim of our work is to describe the accesses at the Emergency Departement (ED) of the Pisa University Hospital (AOUP), Italy, for ARIs, from 2017 to 2021. Methods A retrospective analysis of electronic medical records from the AOUP was conducted from January 2017 to December 2021. The analysis focused on ED admissions and positive lab test results, examining their correlation with ED visits caused by ARI. Multiple linear regression models were used to understand the influence of viral laboratory results on ARI-related ED admissions, adjusting the results for age. The study also assessed the contribution of each virus incausing ARI within different age groups. Results 33,101 (annual average 5,520) ARI admissions in ED were registered, resulting in 7,426 hospital admissions (22.8%, annual average 1,163) with a seasonal pattern between week 42 of each year and week 17 of the following year. The reduction in emergency room admissions in 2020 (weekly average of 78.2, compared to 144.3 in 2017-2019) may be due to the restrictions implemented for the COVID-19 pandemic. Nevertheless, the average weekly rate of ED admissions caused by ARI was 30.8% in 2020, as compared with 21.7% in 2017-2019. Analysis by age group showed a peak of accesses in the last weeks of 2021 for the <1 and 1-4 years old age groups. Conclusions Data on ARI-related admissions provide valuable insights into the dynamic patterns of seasonal air-borne infections and age-related vulnerabilities. This could be useful in directing health policies to identify indicators of future epidemic waves, contributing to the ongoing efforts to enhance preparedness and response strategies for respiratory infections. Key messages • It is important to create an integrated surveillance system to monitor and respond to ARIs. • Strategies should prioritise vaccination campaigns, particularly for RSV and influenza.

Type 2 diabetes individuals have a shorter QRS duration, prolonged QTc interval, flatter T wave, and reduced left ventricular cycle volumes compared to matched controls in the UK Biobank

Abstract Background Type 2 diabetes is associated with an increased risk of heart failure even in the absence of overt coronary artery disease, though underlying mechanisms are unclear. ECG and cardiac magnetic resonance (CMR) imaging provide windows into subclinical changes in myocardial structure and function and could be exploited to understand this. Purpose We investigate differences in ECG and CMR biomarkers in a large cohort of participants with type 2 diabetes, compared to a matched control cohort. We hypothesise that individuals with diabetes may exhibit concurrent differences in biomarkers, reflecting underlying cardiac remodelling. Methods We carried out a pair-matched cross-sectional study to examine the association between type 2 diabetes and multi-modal cardiac biomarkers. The exposure cohort consists of 1,781 UK Biobank participants with an imaging and ECG visit, prevalent type 2 diabetes and no known cardiovascular events at the time of visit. The control cohort is matched on age, sex and body mass index (BMI), and consists of 1,781 participants without diabetes. ECG and CMR biomarkers derived from UK Biobank data were used as outcome variables. We built four multiple multivariate linear regression models, incrementally adjusted for socio-demographic, lifestyle, and clinical covariates. Results Both cohorts were mostly male (63.6%), had a median age of 67 years [IQR: 72-85] and a median BMI of 27.8 kg/m2 [24.6-31.0]. Individuals with type 2 diabetes were more likely to be non-white (7.9% vs 2.7%, p<0.001), current or previous smokers (43.6% vs 40.6%, p=0.04), have lower diastolic blood pressure (79 [72-85] vs 81 [74-88] mmHg, p<0.001), and take cholesterol medication (72.3% vs 27.2%, p<0.001); anti-hypertensives (56.3% vs 27.3%, p<0.001); insulin (13.7% vs 0.1%, p<0.001); and metformin (53.1% vs 0.1%, p<0.001). They had a higher ventricular rate, shorter QRS duration, longer QTc interval, and flatter T wave amplitude, as well as lower left ventricular end-diastolic and end-systolic volumes, among other differences (Table 1). Covariates including cholesterol medication, metformin, and HbA1c were excluded as they were strongly correlated with diabetes, age and/or sex (Pearson correlation coefficient >0.3 or <-0.3). After full adjustment, we found that the exposure variable, diabetes, had a statistically significant association with ventricular rate, QRS duration, T wave offset, T wave amplitude in lead V3, left ventricular stroke volume and global average thickness (p<0.05) (Table 2). Conclusion Our results reveal that individuals with type 2 diabetes exhibit a shorter QRS, longer QTc, and lower end-diastolic and end-systolic volumes, compared to matched controls. This may be indicative of subclinical cardiac abnormalities, both electrophysiological and mechanical. Our association analysis suggests that other factors correlated with diabetes may be also responsible for certain biomarker differences and their underlying causes.

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds using only single models. The data variances in the Rat training and test sets were 1.52 and 1.34, respectively, while for the Mouse, the values were 1.48 and 1.36, respectively. Genetic algorithm (GA) was used to build multiple linear regression (MLR) models and pre-screen descriptors, addressing the “black-box” problem prevalent in ML and enhancing model interpretability. The best ML models for Rat and Mouse achieved approximately 90 % prediction reliability for over 100,000 true untested compounds. Ultimately, a warning list of highly toxic compounds for eight categories of polychlorinated atom-centered fragments (PCACFs) was generated based on the prediction results. The analysis of descriptors revealed that dioxin analogs generally exhibited higher toxicity, because the heteroatoms and ring systems increased structural complexity and formed larger conjugated systems, contributing to greater oral acute toxicity. The present study provides valuable insights for guiding the subsequent in vivo tests, environmental risk assessment and the improvement of global governance system of pollutants.

Functional status is associated with mean right atrial pressure derived from cardiac MRI

Abstract Background Right atrial pressure (RAP) is a key metric in haemodynamic assessment. Elevated RAP is associated with poor prognosis in heart failure and pulmonary hypertension and physiologically with fluid overload. RAP can be measured invasively or non-invasively, but Cardiac MRI (CMR) currently cannot estimate RAP. Purpose To develop a model to estimate RAP from CMR from paired right heart catheter (RHC) and CMR assessments. Assess correlation to WHO functional status prior to prospective clinical validation studies. Methods Patients were recruited to a registry of those referred for assessment of dyspnoea between 2012 and 2020. Inclusion criteria were age >18 years, signs and symptoms of heart failure and adequate CMR image quality. Patients diagnosed with pulmonary arterial hypertension were excluded. RHC and CMR were performed on the same day. CMR 2- and 4- chamber cines were used to measure chamber dimensions, strain, ejection fraction and stroke volume with operator reviewed AI contours in MASS research software. Pearson’s product-moment correlation coefficient (r ) was used to assess relationships between CMR metrics and invasive mRAP. Variables were then included in stepwise multiple linear regression models to predict mRAP. These were compared with receiver-operator curve analysis and DeLong’s test. Kruskal-Wallis test compared median mRAP between WHO functional class groups. Results The cohort of 672 patients was divided based upon invasive mRAP ≤ 8mmHg (44%) and mRAP > 8mmHg (56%). Higher mRAP was associated with increased age, male sex and a higher diastolic blood pressure but there was no difference in rates of heart failure types although the majority had preserved ejection fraction (51% vs 52%) with few having reduced ejection fraction (4.1% vs 8%). Right atrial (RA) dimensions, strain and ejection fraction had the strongest correlation with mRAP with moderate correlation to right ventricular (RV) measurements. Multivariable models contained simple RA dimensions (model 1), RA dimensions and strain (model 2), RA and RV dimensions (model 3) and RA end systolic volume corrected for body mass index and sex (model 4). All models had similar predictive capability (Figure 1 Panel A). RA end systolic volume was isolated in model 1 as the only variable required for mRAP prediction (coefficient = 0.06, p < 0.001). Using a threshold of mRAP >8mmHg ROC analysis demonstrated an area under the curve of 0.78 (95% Confidence interval 0.75 to 0.81) (Figure 1 Panel B). Worse WHO functional Class at time of assessment was significantly associated with increased CMR derived mRAP (P<0.001) (Figure 2). Conclusions mRAP can be estimated with moderate confidence from CMR RA end systolic volume. Limited available clinical data suggest functional status may be predicted by CMR derived mRAP. Further studies on wider populations are required to validate this model externally, refine it and determine its prognostic clinical significance.

Prediction of municipal waste generation using multi-expression programming for circular economy: a data-driven approach.

The existing surge in municipal waste generation (MWG), characterized by swiftly changing and uncontrollable factors, poses a significant challenge to sustainable development. This prompted the need for improved predictive models to guide strategic waste management within the circular economy framework. This study aims to develop a predictive model using multi-expression programming (MEP) to assess MWG. The model was developed using historical data on socioeconomic and environmental factors and validated via comparative analyses with artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) using various evaluation metrics. The parametric and sensitivity analyses of the MEP model were also conducted. The MEP, ANN, RF, and MLR models have a coefficient of determination (R2) (for testing datasets) of 0.977, 0.974, 0.957, and 0.964, respectively. The MEP model is superior in terms of accuracy and performance for the prediction of MWG when compared to the other three models. The sensitivity analysis revealed the relative importance of each input variable in the established MEP model. The novelty of this research lies in the application of MEP to predict MWG and the formulation of a new mathematical model that links socioeconomic and environmental factors with MWG. The model can be used by waste management authorities to optimize waste collection, transportation, and disposal infrastructure for an effective circular economy and sustainable development. This model also aids in the development of effective waste management policies.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.