Regression Coefficients Research Articles

The Number of Antiseizure Medications Taken and not the Lipid Profile was Associated with Seizure Control in Adult Patients with Epilepsy.

Previous studies show changes in lipid metabolism in epilepsy. The aim of this study was to investigate the association between lipid profile and clinical variables in adult patients with epilepsy (APE). Seventy-two APE participated in this pilot study at an outpatient neurology service. The lipid profile (total cholesterol, low-density lipoprotein (LDL) cholesterol, very-low-density lipoproteins (VLDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides), age at disease onset, disease duration, seizures frequency, and the number of antiseizure medications (ASM) used were investigated. Data were analyzed using the Chi-square, Fisher, Mann-Whitney, Spearman coefficient, and logistic regression tests. There were significant differences in HDL (p = 0.0023) and total cholesterol (p = 0.0452) levels in connection with the number of ASM used. There was a significant difference in seizure control among the different numbers of ASM used (p = 0.0382). Higher HDL values were found in females (p = 0.0170). The logistic regression showed that only the number of ASM used was associated with seizure control (p = 0.0408; OR = 2.800; 95% CI = 1.044; 7.509). The number of ASM taken and not the lipid profile was associated with seizure control in APE.

Combining Mendelian Randomization Analysis and 3D-QSAR to Investigate the Effectiveness of a New Series of Hydroxyquinolines in Osteoarthritis.

Osteoarthritis (OA) represents a persistent degenerative joint ailment. As OA advances, profound joint pain coupled with diminished joint function inflicts substantial physical distress and psychological strain on patients. Presently, pharmacological solutions for arthritis remain limited, primarily encompassing analgesics and joint replacement surgical procedures. Hence, non-operative strategies to mitigate osteoarthritis progression have captured significant attention in orthopedic research. This study aims to discern a definitive causal linkage between ADAMTS-4/5 and osteoarthritis through Mendelian randomization analysis. Moreover, it seeks to anticipate the therapeutic efficacy of a suite of emergent hydroxyquinolines for osteoarthritis using the Quantitative Structure-Activity Relationship (QSAR) methodology. Within this study, genetic variants specific to knee osteoarthritis were procured as exposure variables from a genome-wide association study (GWAS). Genetic variant data for ADAMTS-4/5 served as the endpoint to evaluate the causal nexus employing univariate Mendelian randomization. This analysis underpins the hypothesis that ADAMTS-4/5 presents a promising therapeutic target for osteoarthritis management. The suppressive properties of novel hydroxyquinolines against ADAMTS-4/5 were subsequently examined through conformational analyses, underscoring the potential of these compounds as therapeutic candidates for osteoarthritis. IVW outcomes from the Mendelian randomization revealed a significant association of KOA (OR: 1.1675, 95% CI: 1.0003-1.3627, P = 0.0495) with ADAMTS-5. However, KOA (OR: 1.0801, 95% CI: 0.9256-1.2604, P = 0.3278) displayed no evident connection with ADAMTS-4. Notably, the instrumental variables manifested neither heterogeneity nor horizontal pleiotropy. In this research endeavor, 16 pharmacological models were formulated via the CoMSIA method within 3D conformational relationship evaluations. A synergistic interplay of hydrophobic, spatial, and hydrogen-bonded receptor domains emerged as the most predictively potent. The cross-validation coefficient q2 for the optimum model stood at 0.716, with a principal component score of 5, a regression coefficient r2 of 0.971, a standard estimation error of 0.351, and an f-value of 156.951. Such metrics intimate the commendable predictive prowess of our devised CoMSIA models. The research unearthed a robust causal interrelation between ADAMTS-5 and osteoarthritis via Mendelian randomization. Furthermore, a credible drug model targeting ADAMTS-5 was constructed. Collectively, these findings illuminate a path forward in the pursuit of target-specific drugs for osteoarthritis management in subsequent investigations.

REMEDIAL PRACTICES AND STUDENTS COMPETENCES IN MATHEMATICS IN PUBLIC SECONDARY SCHOOLS IN RWANDA A CASE OF GICUMBI DISTRICT

Background: Mathematics plays a crucial role in both vocational success (Jetter, 2017) and everyday life (Umameh, 2011), yet many students face challenges in mastering it. Globally, educational equity remains a priority, with persistent disparities in math education, especially among low-SES students (UNESCO, OECD). Tanzania and Rwanda are actively addressing these challenges to enhance educational outcomes. Materials and Methods: This descriptive survey study involved 171 participants from 32 secondary schools in Gicumbi District: 105 mathematics instructors, 50 subject leaders, 35 head teachers, and 110 students. Data collection employed random sampling, with Slovenes method used to select the study sample. SPSS version 21.0 facilitated analysis, encompassing descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (correlation, regression coefficients). Results and Discussions: Findings revealed strong consensus among respondents on the efficacy of modifying instructional methods (80.0%), using personalized learning materials (63.1%), integrating visual and auditory resources (60.0%), and creating tailored exercises (68.0%) to improve math competencies. Key indicators such as problem-solving (64.3%), method application (96.6%), reasoning (92.3%), and mathematical representation (92.3%) demonstrated significant improvements. Enhanced physical facilities also correlated positively with student outcomes, underscoring the role of supportive environments in fostering mathematical competence. Conclusion: The study emphasizes the effectiveness of adapting teaching methods and environments to enhance students math skills. Customized approaches, including personalized materials and multimedia integration, proved instrumental in improving competencies like problem-solving and reasoning. Statistical analyses consistently supported these findings, highlighting the importance of personalized and diversified teaching strategies in enhancing math education in Gicumbi, Rwanda.

Linear-quadratic Tobit regression model with a change point due to a covariate threshold

This paper considers a linear-quadratic Tobit regression model, which is developed for modelling the mixture structure with a line segment and a quadratic segment intersecting at an unknown change point. Due to the smoothness of such model structure, the regression coefficients and the change point can be obtained by directly maximising the likelihood function. The proposed estimator has rapid convergence rate and high estimation accuracy, without other computation burden. The asymptotic properties for the proposed estimator are derived by using empirical process theory. A sup-likelihood ratio test procedure is developed for testing the existence of a change point, and its limiting distributions are derived. The good finite sample performances of the proposed estimator are illustrated by numerical studies and empirical applications to the MGUS and GDP data.

Complementary Foods Formulated from Orange-flesh Sweet Potato, Soybean, and Date Palm Flour Blends Improves the Proximate, Rheological and Sensory Properties of Children’s Formulas

This study evaluated the proximate, rheological and sensory properties of complementary foods produced from orange flesh sweet potato, soybean and date palm flour blends. Complementary foods were produced from orange flesh sweet potato (OFSP), soybean and date palm flour blends. The samples were formulated by material balancing with all samples having 16% protein/ 100g. The samples were coded as CON, CFA, CFB, CFC, CFD and CFE with sample CON being the control. The samples were subjected to rheological and sensory analysis using standard method. Their proximate composition were; moisture (2.51-7.38%), ash (2.27-3.02%), fibre (2.11-4.22%), fat (9.13-10.11%), protein (15.12-16.11%), and carbohydrate (63.06-65.01%). Rheological properties of the gruels were as follows; consistency index (m) (90.57-186.21 Nsn/m2), Flow behaviour index (n) (0.256-0.411), Coefficient of regression (r2) (0.9765-0.9928) and Standard error (0.318-0.405). The sensory attributes gave the following results; appearance (7.77- 8.07), aroma (7.20-8.63), taste (6.80- 8.23), mouth feel (6.87- 8.27), and overall acceptability (6.80- 8.33) and these results when interpreted on appoint hedonic scale. Higher scores generally indicated better sensory attributes and overall acceptability of the food formulation which was observed in sample CFB and CON. There were significant differences between most of the bread samples at (p< 0.05).

Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses.

Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up. In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up. Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: β=-0.31; diabetes distress: β=-0.33; hypoglycaemia fear: β=-0.27; all p<0.001) but also higher HbA1c (β=0.12; p<0.05). Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach.

Multifactor analysis of the infiltration characteristics of film hole irrigation under muddy water conditions

Film hole irrigation under muddy water conditions is a new and effective water-saving irrigation technology. In order to determine the influence of multiple factors on the infiltration process of film hole irrigation under muddy water conditions, 12 sets of indoor infiltration tests were conducted to investigate the effects of four key influencing factors (muddy water sand content, hole diameter, soil bulk density, and infiltration time) on the infiltration characteristics of irrigation with film holes under muddy water conditions, in this study. Based on the experimental data, accurate and effective soil water infiltration and vertical and horizontal wetting front transport models were constructed. Based on the modeling results, the standard regression coefficients of each influencing factor against the fitted parameters were calculated and the effects of the factors on the fitted parameters were analyzed. Error analysis showed that both models could effectively simulate the soil water infiltration process under experimental conditions. Infiltration time was the dominant factor influencing the cumulative infiltration per unit of film hole area, followed by hole diameter, muddy water sand content and lastly, soil bulk density. Infiltration time was also the main influencing factor of the vertical wetting front transport distance, followed by soil bulk density, muddy water sand content, and lastly, hole diameter. Similarly, infiltration time exerted the greatest effect on the horizontal transport distance, followed by hole diameter, soil bulk density and muddy water sand content. The model validation revealed that both the calculated and measured values were distributed around the 1:1 line, reflecting the accuracy of the models. The results of this study can provide theoretical support for the design of film hole irrigation systems under muddy water conditions.

Estimating inter-regional trade: the radiation model versus the gravity model

The estimation of interregional trade flows within a country is confined to the context of multi-regional input–output models. Despite the remarkable relevance of these trade flows for regional incomes, most countries have almost no official data, with Korea being an exception used here. In addition to survey-based estimation methods, there are non-survey methods, some based on gravity models, which can be used to estimate inter-regional trade flows. This paper compares the gravity model with the novel radiation model. Preliminary results show that the accuracy of the radiation model’s estimation is superior to that of the gravity model, with the invaluable benefit of not needing to estimate regression coefficients, unlike the gravity model. This finding provides a generalisation of the radiation model for estimating interregional trade in contexts where there is no prior multi-regional input–output table without restrictions derived from coefficient estimation as in the gravity model.

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging

Objective. This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics. Approach. We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis. Main results. The proposed model demonstrates a significant linear relationship between the variable x and the slope DVT of the RE Logan plot, and the variable x′ and the slope DVT′ of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of the x′ to the x equal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, the r values for the linear fitting of DVT′ to DVT equal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data. Significance. These results demonstrate the model’s capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

Impact of disease activity patterns on health-related quality of life (HRQoL) in patients with systemic lupus erythematosus (SLE)

ObjectiveTo assess the impact of different disease activity patterns—long quiescent (LQ), chronically active (CA) and relapsing-remitting (RR)—on health-related quality of life (HRQoL) in a cohort of patients with systemic lupus...

LncRNA signature mediates mitochondrial permeability transition-driven necrosis in regulating the tumor immune microenvironment of cervical cancer

Mitochondrial permeability transition (MPT)-driven necrosis (MPTDN) was a regulated variant of cell death triggered by specific stimuli. It played a crucial role in the development of organisms and the pathogenesis of diseases, and may provide new strategies for treating various diseases. However, there was limited research on the mechanisms of MPTDN in cervical cancer (CESC) at present. In this study, Weighted Gene Co-expression Network Analysis (WGCNA) was performed on differentially expressed genes in CESC. The module MEyellow, which showed the highest correlation with the phenotype, was selected for in-depth analysis. It was found that the genes in the MEyellow module may be associated with the tumor immune microenvironment (TIME). Through COX univariate regression and LASSO regression analysis, 6 key genes were identified. These genes were further investigated from multiple perspectives, including their independent diagnostic value, prognostic value, specific regulatory mechanisms in the tumor immune microenvironment, drug sensitivity analysis, and somatic mutation analysis. This study provided a comprehensive exploration of the mechanisms of action of these 6 key genes in CESC patients. And qRT-PCR validation was also conducted. Through COX univariate regression and LASSO coefficient screening of the MEyellow module, 6 key genes were identified: CHRM3-AS2, AC096734.1, BISPR, LINC02446, LINC00944, and DGUOK-AS1. Evaluation of the independent diagnostic value of these 6 key genes revealed that they can serve as independent diagnostic biomarkers. Through correlation analysis among these 6 genes, a potential regulatory mechanism among them was identified. Therefore, a risk prognostic model was established based on the collective action of these 6 genes, and the model showed good performance in predicting the survival period of CESC patients. By studying the relationship between these 6 key genes and the tumor microenvironment of CESC patients from multiple angles, it was found that these 6 genes are key regulatory factors in the tumor immune microenvironment of CESC patients. Additionally, 16 drugs that are associated with these 6 key genes were identified, and 8 small molecule drugs were predicted based on the lncRNA-mRNA network. The 6 key genes can serve as independent biomarkers for diagnosis, and the Risk score of these genes when acting together can be used as an indicator for predicting the clinical survival period of CESC patients. Additionally, these 6 key genes were closely related to the tumor immune microenvironment of CESC patients and were the important regulatory factors in the tumor immune microenvironment of CESC patients.

The Influence of Social Media Marketing of A Beauty Service SME on the Customer Purchase Decision

This study aims to determine the description of Instagram social media marketing and purchasing decisions at MUA Amei Makeup, and to determine the effect of Instagram social media marketing on purchasing decisions at MUA Amei Makeup. This research uses quantitative research methods with descriptive and verification approaches. The data sources used are primary data and secondary data. In collecting data, researchers used observation, interviews and questionnaires. The sampling technique is probability sampling, where each member has the same opportunity to be selected as a sample. The population of this study were MUA Amei Makeup Instagram followers with a total of 1963 people, so a sample of 95 people was obtained. Data was collected using a questionnaire. The data analysis techniques used are descriptive statistics, classical assumption test, simple regression test, t test, and coefficient of determination. The results showed that Instagram social media marketing had a positive effect on purchasing decisions at MUA Amei Makeup by 31.3% while the remaining 68.7% was influenced by other variables such as marketing mix, socio-cultural influences, and communication consisting of advertising, buzz agents and word-of-mouth which were not examined in this study

Effect of Cu incorporation in silicate and modified borate bioglass materials for health applications: insights from synchrotron-based x-ray absorption spectroscopy

This contribution investigates the effect of variable copper incorporation (x = 0.2, 1.0, 2.0, and 4.0) in silicate (45 SiO2, 24.5 CaO, 24.5 Na2O, 6P2O5wt%) and modified borate (45 B2O3, 24.5 CaO, 24.5 Na2O, 6P2O5wt%) bioglass materials to be used for bone bonding applications. X-ray absorption fine structure spectroscopy (XAFS) has been used to determine the oxidation states and local coordination structure of Cu atoms in silicate-based and borate-based glasses at the Cu K-edge (~ 8979 eV). The oxidation states of Cu atoms have been determined by near-edge XAFS (XANES) fingerprinting employing reference standard compounds of Cu. Cu (I) and Cu (II) XANES spectra of the standard reference compounds were linearly combined to fit the normalized μ(E) data of the collected XANES spectra using linear combination fitting (LCF approach). The obtained results prove that most of the silicate glass samples contain Cu2O almost exclusively, while modified borate glass samples contain a significant mixture of Cu2O and CuO phases. According to the literature, the remarkable coexistence of Cu2O and CuO phases within the borate sample, particularly when x = 4, promotes the conversion process to allow the more facile formation of hydroxy carbonate apatite (HCA). The best fit structural parameters derived from extended-XAFS (EXAFS) fitting show that the ratio between Cu (I) and Cu (II) in borate glass agreed well with that extracted from XANES analysis. XANES and EXAFS conclude that borate glass with x = 4 is the most suitable composition for bone bonding applications.

Vagus nerve size determined via ultrasonography is associated with white matter lesions in patients with vascular risk factors.

The cross-sectional area (CSA) of the cervical vagus nerve (VN), as assessed through ultrasonography, might be linked to autonomic nervous system dysfunction. Hypertension is the primary factor associated with cerebral white matter lesions (WMLs), but there is also evidence of a connection with autonomic nervous system dysfunction. However, the associations between WMLs and VN size are unclear. Our objective was to investigate the associations between WMLs and VN size in patients with vascular risk factors. The CSA of the VN was evaluated using carotid ultrasonography in patients with a history of stroke (acute or chronic) and comorbidities (n = 196, 70.2 ± 12.7years). Common carotid artery (CCA) intima-media thickness and interadventitial diameter (IAD) were also measured. The severity of the WMLs was assessed by the Fazekas classification and Scheltens' scale. The CSA of the right VN (2.08 ± 0.65mm2) was significantly greater than that of the CSA of the left VN (1.56 ± 0.44mm2) (P < 0.001). Multiple linear regression analyses revealed that older age, hypertension, increased right CCA IAD, and decreased CSA of the right VN (standardized partial regression coefficient [β] -0.226; P < 0.001) were independently associated with the severity of WMLs (Scheltens' scale). A decreased CSA of the left VN was also associated with the severity of WMLs (β = -0.239; P < 0.001). VN size determined via ultrasonography was associated with the severity of WMLs. While these findings do not establish a causal relationship, they suggest that autonomic nervous system dysfunction is involved in the progression of WMLs.

Knockdown of Anoikis-Associated Gene OCIAD2 Reduces Proliferation and Migration of Glioblastoma Cell Lines.

Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression. We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration. Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration. This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

Tensor-Based Statistical Learning Methods for Diagnosing Product Quality Defects in Multistage Manufacturing Processes

Multistage manufacturing processes with identical stages provide three-dimensional process data in which the first dimension represents the process (control/sensing) variable, the second is the stage, and the third is the measurement/sampling/data acquisition time point. Diagnosing quality faults in such processes often requires the simultaneous identification of crucial process variables and stages associated with product quality anomalies. Most existing diagnosis methods convert 3D data into a 2D matrix, resulting in loss of information and reduced diagnostic accuracy and stability. To address this challenge, we propose a penalized tensor regression model that regresses the product quality index against its 3D process data. For the estimation of high-dimensional regression coefficients with the limited amount of historical data, we apply the CANDECOMP/PARAFAC and Tucker decompositions to the coefficient tensor, which significantly reduces the number of parameters to be estimated. Based on the decompositions, a new regularization term is designed to enable the joint identification of critical process variables and stages. To estimate the parameters, we develop the block coordinate proximal descent algorithm and provide its convergence guarantee. Numerical studies demonstrate that the proposed methods can enhance diagnostic stability and on average improve the diagnostic accuracy by around 20% over existing benchmarks.

Radial force and wire structure determine the onset of covered self-expandable metal stent migration in endoscopic ultrasound-guided hepaticogastrostomy: Measurement of sliding-resistance force using a porcine model.

Self-expandable metal stent (SEMS) migration after endoscopic ultrasound-guided hepaticogastrostomy (EUS-HGS) is a severe complication. The migration risk could be related to the surface friction of SEMS, assumed to be affected by the wire structure and mechanical properties, including radial force (RF); however, their relevance remains unclear. This experimental study aimed to assess the mechanical properties of SEMS involved in the onset of stent migration by measuring the sliding-resistance force (SF) as the SEMS moves through the stomach wall. The SF of seven types of 8-mm diameter SEMS (four braided and three laser-cut types) and porcine stomach wall was measured with a universal testing machine. The SF of each SEMS was measured three times, and the average maximum SF (SFmax) was used for analysis. The correlation between SFmax and RF of each SEMS was evaluated. SFmax and RF showed a very strong positive correlation (r = 0.92). Compared to the regression line predictions in the scatter plots of SFmax and RF, the SFmax of laser-cut and braided type SEMSs had positive and negative residuals, respectively. Selecting a laser-cut type SEMS with a higher RF may more effectively prevent the onset of stent migration against the stomach wall in EUS-HGS.

Blood cytokines as potential predictors of the development of SARS-CoV-2 associated pneumonia in patients with stage II ESSENTIAL hypertension

The search for predictors of the severe course of COVID-19 was relevant both during the pandemic and at the present time. The aim of the study was to analyze the relationship of cytokine levels in patients with EН before SARS-CoV-2 infection with the incidence of coronavirus pneumonia. Materials and methods. From the database of 290 patients with stage II EAG who have been under observation for 8–12 years with annual blood sampling to determine cytokine levels, COVID-19 survivors were selected (mild without pneumonia and moderate to severe, pneumonia CT 1–2, CT-3). Anamnestic levels of IL-1β, IL-1α, IL-1ra, IL-18, IL-18 BP, IL-37, IL-6, sIL-6r, M-CSF, VEGF-A, IL-34 and HMGB1) in the blood serum of patients with stage II EH were analyzed (method ELISA). The analysis of the obtained results was carried out using Stat Soft Statistica 13.5. Results and discussion. Patients with EH and COVID-19 with pneumonia (CT I-II) after SARS-CoV-2 infection 2–6 months before the infectious disease had significantly higher serum levels: IL-1α (p 0.05) and a decrease in IL-37 (p 0.001). During multivariate correlation analysis, a statistically independent relationship between an increase in the incidence of viral pneumonia in patients with stage II EН was confirmed only for IL-37 with a blood level of less than 60.2 pg/ml (regression coefficient — 2.21, standard error — 0.28, t criterion — 6.12, relative risk — 2.52, criterion Walda — 7.92, p = 0.006). When studying circadian rhythms of cytokine content in blood serum in patients of the analyzed groups, calculating the strength of correlations of anamnestic evening IL-37 levels in patients with stage II EР at 19.00–20.00 with the frequency of pneumonia against the background of SARS-CoV-2 infection, it has greater specificity and sensitivity (specificity — 0.75, sensitivity — 0.82) than morning concentrations. Considering that data on circadian rhythms were obtained in a limited number of patients, further monitoring is necessary, which is carried out by our scientific group. It should be noted that patients with essential hypertension are pathogenetically heterogeneous, including in terms of cytokine regulation. The study of this area will make it possible to personalize cytokine phenotypes of the disease and develop new methods for calculating the prognosis of both cardiovascular complications and features of the course of infectious diseases.

Prediction of aggressive tendencies from facial dimension and ratios: A Study on undergraduate students of Northeastern Nigeria

Facial dimensions and ratios are important anthropometric parameters which have been used over the years to define and predict some physical and social characteristics notably among human behaviour like aggression. However, there is a paucity of data regarding this link among the African population, particularly in Northeastern Nigeria where high cases of insurgency, banditry and some aggressive related criminalities are reported daily. The study aimed to determine how linear facial dimensions or ratios can specifically predict a self-reported form of aggression (physical, verbal, anger and hostility) among undergraduate students. A cross-sectional study design was adopted whereby a total of 400 (200 male and 200 female) adult undergraduate students aged between 16 to 30 years were randomly selected. A 2D facial images were used to determine facial biometrics (four linear facial dimensions ((n-sn, sn-gn, go-go and zy-gy) and five facial ratios {upper facial weight/lower facial height (UFW/LFH), upper facial weight/lower facial weight (UFW/LFW), upper facial weight/upper facial height (UFWUFH) and upper facial height/facial height (UFH/FH) and facial weight to height ratio (fWHR) - lower derived from the measured linear dimensions using a Sony digital camera DSC w380 and art face 3 software. Buss and Perry's aggression questionnaire was adopted, and scores for each aggression scale were recorded. Data analysis was done using IBM SPSS software version 22 and Cronbach’s alpha for each scale above 0.70. Pearson’s correlation analysis was used to determine the relationship of facial biometrics with aggressive tendencies. The study observed that lower facial height (sn-gn) significantly correlates with verbal aggression and anger irrespective of sex. Facial height (FH) also correlates with verbal aggression in both sexes, while fWHR correlates significantly with anger (AN) irrespective of sex. More so, lower facial height to facial height ratio (LFH/FH) correlates significantly with verbal aggression (VA) and anger (AN) irrespective of sex but correlates with the same in males only. Similarly, upper facial weight to lower facial height (UFW/LFH) correlates significantly with verbal aggression and anger irrespective of sex and with anger only in males. Stepwise multiple linear regression equations were formed for the parameters which showed a positive correlation with aggression and consequently UFW/LFH and UFW/LFW were the best predictors of aggressive tendencies (P = 0.01). Conclusively, facial characteristics are indicators of one’s mood and can weakly predict aggressive tendencies in an individual and may be useful to predict suitable partners in marriage, friendship, or recruitment exercise.