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Background: Systemic immune-inflammation index (SII) is a novel comprehensive inflammatory marker. Inflammation is associated with impaired lung function. We aimed to explore the possible relationship between SII and lung function to examine the potential of SII in predicting lung function decline. Methods: A cross-sectional survey was conducted using the data of the NHANES from 2007 to 2012. Multiple linear regression models were used to analyze the linear relationship between SII and pulmonary functions. Sensitivity analyses, subgroup analyses, and interaction tests were used to examine the robustness of this relationship across populations. Fitted smooth curves and threshold effect analysis were used to describe the nonlinear relationships. Results: A total of 10,125 patients were included in this study. After adjusting for all covariates, multiple linear regression model analysis showed that high Log2-SII level was significantly associated with decreased FVC(ß, -23.4061; 95% CI, -42.2805- -4.5317), FEV1(ß, -46.7730; 95% CI, -63.3371- -30.2089), FEV1%(ß, -0.7923; 95% CI, -1.1635- -0.4211), FEV1/FVC(ß, -0.6366; 95% CI, -0.8328- -0.4404) and PEF(ß, -121.4468; 95% CI,-164.1939- -78.6998). The negative correlation between Log2-SII and pulmonary function indexes remained stable in trend test and stratified analysis. Inverted U-shaped relationships between Log2-SII and FVC, FEV1, FEV1%, and PEF were observed, while a negative linear correlation existed between FEV1/FVC and Log2-SII. The cutoff values of the nonlinear relationship between Log2-SII and FVC, FEV1, FEV1%, PEF were 8.3736, 8.0688, 8.3745, and 8.5255, respectively. When SII exceeded the critical value, the lung function decreased significantly. Conclusion: This study found a close correlation between SII and pulmonary function indicators. This study investigated the SII threshold when lung functions began to decline in the overall population. SII may become a promising serological indicator for predicting lung function decline. However, prospective studies were needed further to establish the causal relationship between these two factors.
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Mediadores de Inflamación , Inflamación , Pulmón , Encuestas Nutricionales , Valor Predictivo de las Pruebas , Humanos , Masculino , Estudios Transversales , Femenino , Persona de Mediana Edad , Pulmón/fisiopatología , Pulmón/inmunología , Volumen Espiratorio Forzado , Estados Unidos/epidemiología , Adulto , Capacidad Vital , Inflamación/fisiopatología , Inflamación/inmunología , Inflamación/diagnóstico , Inflamación/sangre , Mediadores de Inflamación/sangre , Anciano , Biomarcadores/sangre , Factores de Riesgo , Modelos LinealesRESUMEN
Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.
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Simulación por Computador , Metaanálisis como Asunto , Sesgo de Publicación , Sesgo de Publicación/estadística & datos numéricos , Humanos , Funciones de Verosimilitud , Modelos Lineales , Interpretación Estadística de Datos , Modelos Estadísticos , Sensibilidad y Especificidad , Biometría/métodosRESUMEN
In the mirabegron (MIR) synthesis, the N-nitroso mirabegron (NNM) is obtained during synthetic process of MIR; water is being used in reaction under acidic condition. Nitrite source is from water, and secondary amine source is from MIR as it has secondary amine; NNM is generated as an impurity during the synthesis of MIR. The presence of NNM in MIR could potentially affect its effectiveness. The purpose of this study was to establish a Ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) methodology to identify NNM in MIR samples. The method for NNM analysis was developed on Acquity HSS T3 (100*2.1) mm 1.8 µm column with gradient elution using mobile phase consisted of 0.1% formic acid in water (mobile phase A) and 0.1% formic acid in methanol (mobile phase B). Mass spectrometer with electrospray ionization operated in the MRM mode was used in the analysis of NNM (m/ z 426.20 â 170.00). The UPLC-MS/MS methodology proposed showed a good linearity (0.02 to 0.72 ppm), good system precision (RSD = 0.57%), good method precision (RSD = 0.87%), acceptable accuracy (94.5-116.5%), low detection limit (0.006 ppm) and low quantification limit (0.02 ppm) for NNM. The UPLC-MS/MS methodology proposed can be utilized to assess the quality of MIR sample for the presence of NNM impurity.
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Acetanilidas , Espectrometría de Masas en Tándem , Tiazoles , Espectrometría de Masas en Tándem/métodos , Acetanilidas/análisis , Acetanilidas/química , Cromatografía Líquida de Alta Presión/métodos , Tiazoles/análisis , Tiazoles/química , Reproducibilidad de los Resultados , Límite de Detección , Modelos Lineales , Contaminación de Medicamentos , Compuestos Nitrosos/análisis , Compuestos Nitrosos/química , Cromatografía Líquida con Espectrometría de MasasRESUMEN
Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.
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Recién Nacido de Bajo Peso , Análisis Espacial , Humanos , Pakistán/epidemiología , Análisis por Conglomerados , Recién Nacido , Femenino , Modelos Lineales , Distribución de PoissonRESUMEN
While the association between migration and deteriorated refugee mental health is well-documented, existing research overwhelmingly centers on adult populations, leaving a discernible gap in our understanding of the factors influencing mental health for forcibly displaced children. This focus is particularly noteworthy considering the estimated 43.3 million children who are forcibly displaced globally. Little is known regarding the association between family processes, parental and child wellbeing for this population. This study addresses these gaps by examining the relationship between parental mental health and child mental health among refugees experiencing transmigration. We conducted in-person structured survey interviews with 120 parent-adolescent dyads living in the Trichy refugee camp in Tamil Nadu, India. Descriptive, multivariate analysis (hierarchical regression), and Machine Learning Algorithm (XGBOOST) were conducted to determine the best predictors and their importance for child depressive symptoms. The results confirm parental mental health and child behavioral and emotional factors are significant predictors of child depressive symptoms. While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. The study's findings amplify the critical need for mental health resources for both parents and children, as well as parenting interventions inside refugee camps.
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Salud Mental , Campos de Refugiados , Refugiados , Humanos , Femenino , Masculino , Refugiados/psicología , India , Niño , Adolescente , Adulto , Depresión/epidemiología , Depresión/psicología , Aprendizaje Automático , Padres/psicología , Persona de Mediana Edad , Modelos LinealesRESUMEN
Background: The chronic inflammatory immune response is a significant factor in the pathogenesis of benign gynecological diseases. The systemic immunoinflammatory index (SII) and the platelet-to-lymphocyte ratio (PLR) are commonly available biomarkers of inflammation. However, evidence of the relationship between SII and PLR in patients with adenomyosis is limited. This study aimed to investigate the relationship between SII and PLR in patients with adenomyosis. Methods: This cross-sectional study included 483 patients with adenomyosis who were first diagnosed at our institution between January 2019 and December 2021. Basic patient clinical information and inflammatory factors were collected for univariate analysis, smoothed curve fitting, and multivariate segmented linear regression. Results: The results of the univariate analysis showed a significant positive correlation between PLR levels and SII (P < 0.001). In addition, a nonlinear relationship between PLR and SII was tested using a smoothed curve fit after adjusting for potential confounders. Multiple segmented linear regression models showed a significant relationship between SII and PLR in both SII < 1,326.47 (ß 0.14, 95% CI: 0.12, 0.16; P < 0.0001) and >1,326.47 (ß 0.02, 95% CI: -0.01, 0.05; P = 0.2461). Conclusions: In conclusion, this study showed a nonlinear relationship between SII and PLR in patients with uterine adenomyosis. An increase in serum PLR levels correlates with an increase in SII before SII levels reach an inflection point.
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Adenomiosis , Plaquetas , Linfocitos , Humanos , Adenomiosis/sangre , Femenino , Estudios Transversales , Adulto , Persona de Mediana Edad , Inflamación/sangre , Modelos Lineales , Biomarcadores/sangre , Recuento de PlaquetasRESUMEN
China's polyester textile industry is one of the notable contributors to national economy. This paper takes polyester yarn, core raw material in polyester textile industry chain, as research object, and deeply explores its price indicators and risk hedging mechanisms through multiple linear regression models and Holt-Winters approaches. It is worth mentioning that with continuous development of digital technology, digital transformation of production lines and warehouses has become an important development feature in various industries. This study also actively complies with this trend, and innovatively incorporates the upstream and downstream production line start-up rates into price prediction model. Through this initiative, we can more comprehensively consider the impact of supply and demand changes on price of polyester yarn, thus making prediction results more closely reflect the actual market situation. This quantitative analysis method undoubtedly provides new ideas for enterprises to better grasp market dynamics in digital era.
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Poliésteres , Poliésteres/química , Modelos Lineales , Industria Textil , Comercio/economía , China , TextilesRESUMEN
This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Nigde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.
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Peso Corporal , Aprendizaje Automático , Oveja Doméstica , Animales , Modelos Lineales , Femenino , Masculino , Oveja Doméstica/fisiología , Oveja Doméstica/crecimiento & desarrollo , India , Algoritmos , Ovinos , Peso al NacerRESUMEN
OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.
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Puntos Anatómicos de Referencia , Inteligencia Artificial , Cefalometría , Ortodoncia Correctiva , Humanos , Cefalometría/métodos , Masculino , Femenino , Adulto , Ortodoncia Correctiva/métodos , Resultado del Tratamiento , Redes Neurales de la Computación , Adulto Joven , Adolescente , Modelos Lineales , Proceso Alveolar/anatomía & histología , Proceso Alveolar/diagnóstico por imagen , Análisis de los Mínimos CuadradosRESUMEN
OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.
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Puntos Anatómicos de Referencia , Inteligencia Artificial , Cefalometría , Procedimientos Quirúrgicos Ortognáticos , Humanos , Femenino , Cefalometría/métodos , Masculino , Procedimientos Quirúrgicos Ortognáticos/métodos , Modelos Lineales , Resultado del Tratamiento , Adulto , Adulto Joven , Adolescente , Redes Neurales de la Computación , Algoritmos , Estudios Retrospectivos , Análisis de los Mínimos Cuadrados , PredicciónRESUMEN
The aim of this study was to examine the association between fluoride exposure and bone mineral density (BMD) in children and adolescents. We used data from the National Health and Nutrition Examination Survey (NHANES) 2015-2016. The fluoride concentrations in the water samples, plasma samples, and urine samples were measured electrometrically using an ion-specific electrode. Total body less head BMD (TBLH BMD) was measured using dual-energy X-ray absorptiometry (DXA). Weighted generalized linear regression models and restricted cubic splines (RCS) regression models were used to analyze the relationships between the three types of fluoride exposure and TBLH BMD. We performed subgroup analyses stratified by sex. A total of 1413 US children and adolescents were included in our study. In our linear regression models, we found inverse associations between fluoride concentrations in water and plasma and TBLH BMD. Additionally, we discovered a non-linear association between fluoride concentrations in water and plasma and TBLH BMD. No significant association or non-linear relationship was found between urine fluoride levels and TBLH BMD. This nationally representative sample study provides valuable insight into the intricate connection between fluoride exposure and skeletal health in children and adolescents.
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Densidad Ósea , Fluoruros , Encuestas Nutricionales , Humanos , Niño , Fluoruros/orina , Fluoruros/sangre , Fluoruros/efectos adversos , Adolescente , Densidad Ósea/efectos de los fármacos , Masculino , Femenino , Proyectos Piloto , Estados Unidos , Absorciometría de Fotón , Exposición a Riesgos Ambientales/efectos adversos , Exposición a Riesgos Ambientales/análisis , Estudios Transversales , Modelos LinealesRESUMEN
Obeticholic acid (OCA), a semisynthetic bile acid derivative, was approved for its therapeutic use in primary biliary cirrhosis. OCA has a enterohepatic circulation and host-gut microbiota metabolic interaction, which produce various metabolites. Such metabolites, especially structural isomers of OCA, together with the need to achieve idea lower limit of quantitation (LLOQ) with minimum matrix interference, bring about significant difficulties to the bioanalysis of OCA. Herein, by applying a combination of solid-phase extraction (SPE) and ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), we introduced an approach for the bioanalysis of OCA along with its two major metabolites-glyco-OCA (GOA) and tauro-OCA (TOA) in human plasma, the full validation results of which showed excellent performance. The quantitative range is 0.2506 â¼ 100.2 ng/mL for OCA, 0.2500 â¼ 100.0 ng/mL for GOA, as well as 0.1250 â¼ 50.00 ng/mL for TOA, respectively. This method was successfully applied to the pharmacokinetic studies in healthy subjects following administration of OCA tablets.
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Ácido Quenodesoxicólico , Límite de Detección , Comprimidos , Espectrometría de Masas en Tándem , Humanos , Espectrometría de Masas en Tándem/métodos , Cromatografía Líquida de Alta Presión/métodos , Ácido Quenodesoxicólico/análogos & derivados , Ácido Quenodesoxicólico/sangre , Ácido Quenodesoxicólico/farmacocinética , Ácido Quenodesoxicólico/química , Reproducibilidad de los Resultados , Modelos Lineales , Extracción en Fase Sólida/métodos , MasculinoRESUMEN
Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.
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Cianobacterias , Monitoreo del Ambiente , Eutrofización , Lagos , Tecnología de Sensores Remotos , Cianobacterias/crecimiento & desarrollo , Monitoreo del Ambiente/métodos , Lagos/microbiología , China , Modelos LinealesRESUMEN
Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model's prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.
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Algoritmos , Productos Agrícolas , Fluoruros , Contaminantes del Suelo , Productos Agrícolas/química , Productos Agrícolas/metabolismo , Fluoruros/análisis , Contaminantes del Suelo/análisis , Suelo/química , Monitoreo del Ambiente/métodos , Modelos Lineales , Bosques AleatoriosRESUMEN
Recent evidence suggests that systemic conditions, particularly those associated with inflammation, can affect erythrocyte deformability in the absence of haematological conditions. In this exploratory study, we investigated the relationship between systemic inflammatory status and erythrocyte deformability (using osmotic gradient ektacytometry) in a heterogenous study population consisting of individuals with no medical concerns, chronic conditions, and acute illness, providing a wide range of systemic inflammation severity. 22 participants were included in a prospective observational study. Maximum Elongation Index (EImax) in ektacytometry served as the readout for erythrocyte deformability. Inflammatory status was assessed using C-reactive protein (CRP) and self-reported symptoms associated with inflammatory activation (Sickness Questionnaire Scores, SicknessQ). In a univariate linear regression, both CRP and SicknessQ scores significantly predicted EImax (CRP: F(1,20) = 7.751, p < 0.05 (0.011), R2 = 0.279; SicknessQ: F(1,18) = 4.831, p < 0.05 (0.041), R2 = 0.212). Sensitivity analyses with multivariable linear regression correcting for age showed concordant findings. Results suggest a linear relationship between erythrocyte deformability and biochemical and clinical markers of systemic inflammation. Replication of findings in a larger study, and mechanisms and clinical consequences need further in investigation.
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Proteína C-Reactiva , Deformación Eritrocítica , Inflamación , Humanos , Inflamación/sangre , Masculino , Femenino , Persona de Mediana Edad , Proteína C-Reactiva/análisis , Adulto , Estudios Prospectivos , Anciano , Biomarcadores/sangre , Eritrocitos/metabolismo , Eritrocitos/patología , Modelos LinealesRESUMEN
In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.
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Insecticidas , Aceites Volátiles , Pediculus , Relación Estructura-Actividad Cuantitativa , Aceites Volátiles/química , Aceites Volátiles/farmacología , Pediculus/efectos de los fármacos , Pediculus/química , Animales , Insecticidas/química , Insecticidas/farmacología , Modelos Lineales , HumanosRESUMEN
The association between metal mixtures and kidney function has been reported. However, reports on the mechanism of metal toxicity were limited. Oxidative stress was reported as a possible cause. This study aimed to determine the association between of kidney function and metals, such as arsenic (As), cadmium (Cd), cobalt (Co), copper (Cu), lead (Pb), selenium (Se), and zinc (Zn), and to explore the possible mediating role of tumor necrosis factor alpha (TNF-α) between metal toxicity and kidney function. In this study, we recruited 421 adults from a health examination. The concentration of blood metals was analyzed using inductively coupled plasma mass spectrometry. We used linear regression models to assess the association between metals and TNF-α. Then, mediation analysis was applied to investigate the relationship between metal exposure, TNF-α, and kidney function. In univariate linear regression, blood As, Cd, Co, Cu, Pb, and Zn levels significantly increased TNF-α and decreased kidney function. Higher blood As and Pb levels significantly increased TNF-α in multivariable linear regressions after adjusting for covariates. We found that blood levels of As (coefficients = -0.021, p = 0.011), Pb (coefficients = -0.060, p < 0.001), and Zn (coefficients = -0.230, p < 0.001) showed a significant negative association with eGFR in the multiple-metal model. Furthermore, mediation analysis showed that TNF-α mediated 41.7â¯%, 38.8â¯%, and 20.8â¯% of blood Cd, As and Pb, respectively. Among the essential elements, TNF-α mediated 24.5â¯%, 21.5â¯% and 19.9â¯% in the effects of blood Co, Cu, and Zn on kidney function, respectively. TNF-α, acting as a mediator, accounted for 20.1â¯% of the contribution between the WQS score of metal mixtures and the eGFR (pâ¯<â¯0.001). This study suggested that TNF-α may be a persuasive pathway mediating the association between metals and kidney function. Inflammation and kidney injury could be the underlying mechanisms of metal exposure. However, there is still a need to clarify the biochemical mechanism in follow-up studies.
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Riñón , Análisis de Mediación , Metales Pesados , Factor de Necrosis Tumoral alfa , Factor de Necrosis Tumoral alfa/sangre , Humanos , Masculino , Femenino , Riñón/efectos de los fármacos , Persona de Mediana Edad , Metales Pesados/sangre , Metales Pesados/toxicidad , Adulto , Contaminantes Ambientales/sangre , Contaminantes Ambientales/toxicidad , Exposición a Riesgos Ambientales/efectos adversos , Modelos Lineales , Arsénico/sangre , Arsénico/toxicidad , Metales/sangre , Metales/toxicidadRESUMEN
PURPOSE: Understanding how early adaptive schemas, cognitive flexibility, and emotional regulation influence eating disorder (ED) symptoms, and whether this differs across diagnostic subtypes is critical to optimising treatment. The current study investigated the relationship between these variables and ED symptomology in individuals self-reporting an ED diagnosis and healthy controls. METHODS: A dataset of 1576 online survey responses yielded subsamples for anorexia nervosa (n = 155), bulimia nervosa (n = 55), binge eating disorder (n = 33), other specified feeding or eating disorder (n = 93), and healthy participants (n = 505). The hierarchical linear regression analysis included Eating Disorder Examination Questionnaire 6.0 Global Score as the dependent variable; Young Positive Schema Questionnaire, Emotional Regulation Questionnaire, and Cognitive Flexibility Inventory subscale scores as the independent variables; and demographic measures as the covariates. RESULTS: The number of significant predictors varied considerably by ED sub-group. Amongst the anorexia nervosa, bulimia nervosa, and healthy subsamples, the adaptive schema Self-Compassion and Realistic Expectations was associated with lower ED symptom severity. In comparison, age and body mass index were the strongest predictors for binge eating disorder, whilst the Expressive Suppression (a subscale of the Emotional Regulation Questionnaire) was the strongest predictor for other specified feeding or eating disorders. CONCLUSION: Early adaptive schemas, cognitive flexibility, and emotional regulation vary across ED subtype, suggesting the need for tailored treatment that disrupts the self-reinforcing cycle of ED psychopathology. Future research investigating how early adaptive schemas may predict or be associated with treatment response across diagnostic subtypes is needed. LEVEL OF EVIDENCE: Level IV, evidence obtained from multiple time-series with or without the intervention, such as case studies.
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Adaptación Psicológica , Cognición , Regulación Emocional , Trastornos de Alimentación y de la Ingestión de Alimentos , Humanos , Femenino , Adulto , Trastornos de Alimentación y de la Ingestión de Alimentos/psicología , Trastornos de Alimentación y de la Ingestión de Alimentos/diagnóstico , Regulación Emocional/fisiología , Adulto Joven , Cognición/fisiología , Adolescente , Masculino , Encuestas y Cuestionarios , Bulimia Nerviosa/psicología , Bulimia Nerviosa/diagnóstico , Anorexia Nerviosa/psicología , Anorexia Nerviosa/diagnóstico , Modelos Lineales , Persona de Mediana EdadRESUMEN
OBJECTIVE: To analyze trends in epidemiological risk of leprosy in Goiás state, Brazil, and its health macro-regions, between 2010 and 2021. METHOD: This is a time series analysis of the composite leprosy epidemiological risk index in Goiás. We used cases held on the Notifiable Health Conditions Information System for calculating indicators separately and risk, classified as high, medium, low and very low. Trends were analyzed using Prais-Winsten linear regression and risk maps were produced. RESULTS: Goiás showed high leprosy endemicity (24.8 cases/100,000 inhabitants) and medium epidemiological risk between 2019 and 2021 (0.58). A stationary trend was found (annual percentage change, 0.50; 95% confidence interval, -3.04; 4.16) for risk of leprosy in Goiás as a whole and in its Central-West and Central-Southeast macro-regions. CONCLUSION: There is need for actions to reduce the epidemiological risk of leprosy, especially where its trend is stationary, this includes early screening for new cases and health education. MAIN RESULTS: Leprosy persists in Goiás state, Brazil, in an endemic form, with heterogeneous distribution. There has been a reduction in the number of municipalities with high epidemiological risk of leprosy, but challenges include active transmission and late diagnosis. IMPLICATIONS FOR SERVICES: Long-term strategies for prevention, early detection, treatment and monitoring of people with leprosy and their contacts are needed. PERSPECTIVES: It is crucial to strengthen health policies targeting leprosy in Goiás state, prioritizing continuing education and training programs for health professionals working in the entire territory.
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Enfermedades Endémicas , Lepra , Humanos , Brasil/epidemiología , Lepra/epidemiología , Enfermedades Endémicas/estadística & datos numéricos , Modelos Lineales , Factores de Tiempo , Factores de Riesgo , Notificación de Enfermedades/estadística & datos numéricosRESUMEN
INTRODUCTION: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. METHODS: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. RESULTS: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001). CONCLUSION: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.