Association between the practice of fitness-related exercises and body image dissatisfaction in adolescents from Curitiba (PR), Brazil / Associação entre a prática de exercícios físicos relacionados ao fitness e insatisfação com a imagem corporal em adolescentes de Curitiba (PR), Brasil

ABSTRACT Objective: The aim of this study was to analyze the association between participation in fitness-related exercises (FRE) and body image dissatisfaction (BID) in adolescents and evaluate the interaction between physical exercise and nutritional status in this association. Methods: A cross-sectional study was conducted in 2015 involving 799 adolescents (10-16 years old) from 14 public schools in Curitiba (PR), Brazil. BID was assessed using the Body Shape Questionnaire and the Silhouette Scale. The FRE was classified as "does not practice," "practices ≤300 min/week," and "practices >300 min/week" by the Physical Activity Questionnaire for Adolescents. Poisson and multinomial logistic regressions, adjusted for sex, sexual maturation, and nutritional status analyzed the association of FRE and BID. Results: The BID prevalence was 28.3%; 52.4% of the adolescents wanted to reduce their silhouettes; and 48.7% did not practice FRE. Adolescents who practiced FRE >300 min/week had a 28% higher prevalence for some level of BID (PR 1.28; 95%CI 1.08-1.52) and a 46% lower chance of wanting to reduce silhouettes (OR 0.54; 95%CI 0.35-0.82), compared to nonpractitioners. There was no interaction between FRE and nutritional status in association with BID. Conclusions: The adolescents who practice FRE >300 min/week are likely to have some level of BID and are less likely to report the desire to increase their silhouettes, regardless of their nutritional status.

RESUMO Objetivo: Analisar a associação entre a participação em exercícios físicos relacionados ao fitness (EFRF) e a insatisfação com a imagem corporal (IIC) em adolescentes e avaliar a interação entre os exercícios físicos e o estado nutricional nesta associação. Métodos: Estudo transversal realizado em 2015 com 799 adolescentes (10 a 16 anos) de 14 escolas públicas de Curitiba (PR), Brasil. A IIC foi avaliada por meio do Body Shape Questionnaire e da Escala de Silhuetas. A participação em EFRF foi avaliada pelo Questionário de Atividade Física para Adolescentes e classificada em "não pratica", "pratica ≤300 minutos/semana" e "pratica >300 minutos/semana". As regressões de Poisson e logística multinomial, ajustadas por sexo, maturação sexual e estado nutricional, analisaram a associação entre EFRF e IIC. Resultados: A prevalência de IIC foi de 28,3%; 52,4% dos adolescentes queriam reduzir a silhueta e 48,7% não praticavam a EFRF. Adolescentes que praticavam EFRF >300 minutos/semana tiveram prevalência 28% maior para algum nível de IIC (razão de prevalência — RP 1,28; intervalo de confiança de 95% — IC95% 1,08-1,52) e chance 46% menor de querer reduzir silhuetas (OR 0,54; 95IC% 0,35-0,82), comparados aos não praticantes. Não houve interação entre os EFRF e o estado nutricional na associação com IIC. Conclusões: Os adolescentes que praticam EFRF >300 minutos/semana estão mais propensos a apresentar algum nível de IIC e têm menores chances de reportar o desejo de aumentar silhuetas, independentemente do estado nutricional.

A new screening of preterm birth in gestation with short cervix after pessary plus progesterone.

Objective: This study aims to create a new screening for preterm birth < 34 weeks after gestation with a cervical length (CL) ≤ 30 mm, based on clinical, demographic, and sonographic characteristics. Methods: This is a post hoc analysis of a randomized clinical trial (RCT), which included pregnancies, in middle-gestation, screened with transvaginal ultrasound. After observing inclusion criteria, the patient was invited to compare pessary plus progesterone (PP) versus progesterone only (P) (1:1). The objective was to determine which variables were associated with severe preterm birth using logistic regression (LR). The area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV) were calculated for both groups after applying LR, with a false positive rate (FPR) set at 10%. Results: The RCT included 936 patients, 475 in PP and 461 in P. The LR selected: ethnics white, absence of previous curettage, previous preterm birth, singleton gestation, precocious identification of short cervix, CL < 14.7 mm, CL in curve > 21.0 mm. The AUC (CI95%), sensitivity, specificity, PPV, and PNV, with 10% of FPR, were respectively 0.978 (0.961-0.995), 83.4%, 98.1%, 83.4% and 98.1% for PP < 34 weeks; and 0.765 (0.665-0.864), 38.7%, 92.1%, 26.1% and 95.4%, for P < 28 weeks. Conclusion: Logistic regression can be effective to screen preterm birth < 34 weeks in patients in the PP Group and all pregnancies with CL ≤ 30 mm.

Environmental factors and management practices associated with beef cattle carcass quality in the mid-west of Brazil.

The "Precoce MS" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades "AAA" and "AA." Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as "AAA" or "AA") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an R 2 of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.

Individual, household and institutional level predictors of waste sorting: Evidence for Ecuador.

The rapid increase in waste generation in developing countries presents significant challenges, necessitating effective waste management strategies. This study examines the influence of individual, household and institutional factors on waste sorting behaviours in Ecuador, employing an ordered logistic regression model. Data were sourced from the 2019 National Multipurpose Household Survey (NMHS) and the Census of Economic Environmental Information in Decentralised Autonomous Governments (CEEIGAD). The NMHS uses a two-stage probabilistic sampling methodology, with census sectors as the primary sampling units and households as the secondary units. After excluding outliers and selecting individuals aged 15-65 years, the final sample consisted of 8601 households, including 26,175 individuals. The findings reveal that personal attributes such as gender, ethnicity, age, marital status and environmental concern significantly influence waste sorting behaviours. Household characteristics, including urban or rural location, are also critical. Institutional factors, such as municipal regulations, waste collection fees and waste separation at source, play essential roles in promoting waste separation. The study highlights the necessity for targeted governmental policies. Recommendations include improving environmental education, increasing sorting infrastructure in urban areas and ensuring waste collection systems maintain the separation of waste streams.

Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19.

BACKGROUND: This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. METHODS: A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database. RESULTS: Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07-2.05), 0.81 (0.72-0.90), 9.13 (3.29-28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2-9.8] versus 9.6 [6.8-12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%). CONCLUSIONS: In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. GOV IDENTIFIER: NCT05663528.

Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study.

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

Brain metastasis risk prediction model in females with hormone receptor-positive breast cancer.

BACKGROUND: Breast cancer is a leading cause of cancer-related deaths in females, and the hormone receptor-positive subtype is the most frequent. Breast cancer is a common source of brain metastases; therefore, we aimed to generate a brain metastases prediction model in females with hormone receptor-positive breast cancer. METHODS: The primary cohort included 3,682 females with hormone receptor-positive breast cancer treated at a single center from May 2009 to May 2020. Patients were randomly divided into a training dataset (n = 2,455) and a validation dataset (n = 1,227). In the training dataset, simple logistic regression analyses were used to measure associations between variables and the diagnosis of brain metastases and to build multivariable models. The model with better calibration and discrimination capacity was tested in the validation dataset to measure its predictive performance. RESULTS: The variables incorporated in the model included age, tumor size, axillary lymph node status, clinical stage at diagnosis, HER2 expression, Ki-67 proliferation index, and the modified Scarff-Bloom-Richardson grade. The area under the curve was 0.81 (95 % CI 0.75-0.86), p < 0.001 in the validation dataset. The study presents a guide for the clinical use of the model. CONCLUSION: A brain metastases prediction model in females with hormone receptor-positive breast cancer helps assess the individual risk of brain metastases.

The generalized odd log-logistic-G regression with interval-censored survival data.

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.

Modeling of fruit growth of tangor murcott and incidence of environmental factors on the estimated parameters / Modelación de crecimiento de frutos de tangor murcott e incidencia de factores medioambientales sobre los parámetros estimados

ABSTRACT Fruit production forecasts are a tool to plan the harvest and improve market strategies. To carry it out, it is essential to have information about the behavior of fruit development over time. The objective of this work was to find the mathematical-statistical model that best describes the growth pattern of tangor murcott fruit (Citrus reticulata x C. sinensis 'Murcott') and analyze how it is affected by environmental conditions. For this, in nine orchards, located in four locations in the province of Corrientes, Argentina, the equatorial diameter of 2,053 fruit from 82 days after full flowering to harvest were periodically registered during five seasons. The nonlinear models were compared: Logistic, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, and their respective re-parameterizations. The magnitudes of nonlinearity measures, coefficient of determination and estimates of residual deviation were considered as the main goodness-of-fit criteria. The selected model-parameterization combination was the fifth parameterization of the Logistic model with random effects on its three parameters. An Analysis of Variance model on the estimates of these parameters for each fruit showed that orchard and season factors were an important source of variability, mainly in those related to the initial size of the fruit and their growth rate. These results will allow the construction of growth tables, which in addition to making yield predictions, can be used to estimate fruit size distribution at harvest and improve the cultural practice of manual fruit thinning.

RESUMEN Los pronósticos de producción de fruta son una herramienta para planificar la cosecha y mejorar estrategias de mercado. Para su realización es imprescindible contar con información acerca del desarrollo de los frutos a lo largo del tiempo. El objetivo del presente trabajo fue encontrar el modelo matemático-estadístico que mejor describa el patrón de crecimiento de frutos tangor murcott (Citrus reticulata x C. sinensis 'Murcott') y analizar cómo es afectado por condiciones medioambientales. En nueve huertos, ubicados en cuatro localidades en la provincia de Corrientes, Argentina, se registró durante cinco temporadas el diámetro ecuatorial de 2053 frutos desde los 82 días después de plena floración hasta el momento de cosecha. Se compararon los modelos no lineales: Logístico, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, y sus respectivas re-parameterizaciones. Como principales criterios de bondad de ajuste se consideraron las magnitudes de medidas de no linealidad, coeficiente de determinación y estimaciones del desvío residual. La combinación modelo-parametrización seleccionada fue la quinta parametrización del modelo Logístico con efectos aleatorios en sus tres parámetros. Un modelo de análisis de la variancia sobre las estimaciones de estos parámetros para cada fruto mostró que los factores huerto y temporada eran una importante fuente de variabilidad, principalmente en los relacionados con el tamaño inicial de los frutos y su tasa de crecimiento. Estos resultados permitirán construir tablas de crecimiento, que además de realizar predicciones de rendimientos, podrán ser utilizadas para estimar distribución de tamaños de fruto a cosecha y mejorar la práctica cultural de raleo.

Streets classification models by urban features for road traffic noise estimation.

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Validation and recalibration of sex estimation methods using pubic nonmetric traits for the Chilean population.

Chile had a violent military coup (1973-1990) that resulted in 3,000 victims declared detained, missing or killed; many are still missing and unidentified. Currently, the Human Rights Unit of the Forensic Medical Service in Chile applies globally recognised forensic anthropological approaches, but many of these methods have not been validated in a Chilean sample. As current research has demonstrated population-specificity with extant methods, the present study aims to validate sex estimation methods in a Chilean population and thereafter establish population-specific equations. A sample of 265 os coxae of known age and sex of adult Chileans from the Santiago Subactual Osteology Collection were analysed. Visual assessment and scoring of the pelvic traits were performed in accordance with the Phenice (1969) and Klales et al. (2012) methods. The accuracy of Phenice (1969) in the Chilean sample was 96.98%, with a sex bias of 7.68%. Klales et al. (2012) achieved 87.17% accuracy with a sex bias of -15.39%. Although both methods showed acceptable classification accuracy, the associated sex bias values are unacceptable in forensic practice. Therefore, six univariate and eight multivariate predictive models were formulated for the Chilean population. The most accurate univariate model was the ventral arc at 96.6%, with a sex bias of 5.2%. Classification accuracy using all traits was 97.0%, with a sex bias of 7.7%. This study provides Chilean practitioners a population-specific morphoscopic standard with associated classification probabilities acceptable to accomplish legal admissibility requirements in human rights and criminal cases specific to the second half of the 20th century.

Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic.

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.

Inhibitory action of antibiotics on Kluyveromyces marxianus.

A bioassay containing Kluyveromyces marxianus in microtiter plates was used to determine the inhibitory action of 28 antibiotics (aminoglycosides, beta-lactams, macrolides, quinolones, tetracyclines and sulfonamides) against this yeast in whey. For this purpose, the dose-response curve for each antibiotic was constructed using 16 replicates of 12 different concentrations of the antibiotic. The plates were incubated at 40°C until the negative samples exhibited their indicator (5-7h). Subsequently, the absorbances of the yeast cells in each plate were measured by the turbidimetric method (λ=600nm) and the logistic regression model was applied. The concentrations causing 10% (IC10) and 50% (IC50) of growth inhibition of the yeast were calculated. The results allowed to conclude that whey contaminated with cephalosporins, quinolones and tetracyclines at levels close to the Maximum Residue Limits inhibits the growth of K. marxianus. Therefore, previous inactivation treatments should be implemented in order to re-use this contaminated whey by fermentation with K. marxianus.

Logistic regression and other statistical tools in diagnostic biomarker studies.

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

The Effect of Aortic Angulation on Clinical Outcomes of Patients Undergoing Transcatheter Aortic Valve Replacement.

INTRODUCTION: The aim of this study was to assess the impact of aortic angulation (AA) on periprocedural and in-hospital complications as well as mortality of patients undergoing Evolut™ R valve implantation. METHODS: A retrospective study was conducted on 264 patients who underwent transfemoral-approach transcatheter aortic valve replacement with self-expandable valve at our hospital between August 2015 and August 2022. These patients underwent multislice computer tomography scans to evaluate AA. Transcatheter aortic valve replacement endpoints, device success, and clinical events were assessed according to the definitions provided by the Valve Academic Research Consortium-3. Cumulative events included paravalvular leak, permanent pacemaker implantation, new-onset stroke, and in-hospital mortality. Patients were divided into two groups, AA ≤ 48° and AA > 48°, based on the mean AA measurement (48.3±8.8) on multislice computer tomography. RESULTS: Multivariable logistic regression analysis was performed to identify predictors of cumulative events, utilizing variables with a P-value < 0.2 obtained from univariable logistic regression analysis, including AA, age, hypertension, chronic renal failure, and heart failure. AA (odds ratio [OR]: 1.73, 95% confidence interval [CI]: 0.89-3.38, P=0.104), age (OR: 1.04, 95% CI: 0.99-1.10, P=0.099), hypertension (OR: 1.66, 95% CI: 0.82-3.33, P=0.155), chronic renal failure (OR: 1.82, 95% CI: 0.92-3.61, P=0.084), and heart failure (OR: 0.57, 95% CI: 0.27-1.21, P=0.145) were not found to be significantly associated with cumulative events in the multivariable logistic regression analysis. CONCLUSION: This study demonstrated that increased AA does not have a significant impact on intraprocedural and periprocedural complications of patients with new generation self-expandable valves implanted.

On the Cut-Off Value of the Anteroposterior Diameter of the Midbrain Atrophy in Spinocerebellar Ataxia Type 2 Patients.

(1) Background: Spinocerebellar ataxias (SCA) is a term that refers to a group of hereditary ataxias, which are neurological diseases characterized by degeneration of the cells that constitute the cerebellum. Studies suggest that magnetic resonance imaging (MRI) supports diagnoses of ataxias, and linear measurements of the aneteroposterior diameter of the midbrain (ADM) have been investigated using MRI. These measurements correspond to studies in spinocerebellar ataxia type 2 (SCA2) patients and in healthy subjects. Our goal was to obtain the cut-off value for ADM atrophy in SCA2 patients. (2) Methods: This study evaluated 99 participants (66 SCA2 patients and 33 healthy controls). The sample was divided into estimations (80%) and validation (20%) samples. Using the estimation sample, we fitted a logistic model using the ADM and obtained the cut-off value through the inverse of regression. (3) Results: The optimal cut-off value of ADM was found to be 18.21 mm. The area under the curve (AUC) of the atrophy risk score was 0.957 (95% CI: 0.895-0.991). Using this cut-off on the validation sample, we found a sensitivity of 100.00% (95% CI: 76.84%-100.00%) and a specificity of 85.71% (95% CI: 42.13%-99.64%). (4) Conclusions: We obtained a cut-off value that has an excellent discriminatory capacity to identify SCA2 patients.

Predictors of trimodality therapy in patients with muscle-invasive bladder cancer and effect on survival.

BACKGROUND: Due to its unique advantages over radical cystectomy (RC), trimodality therapy (TMT) is increasingly being utilized by patients diagnosed with muscle-invasive bladder cancer (MIBC) who are not suitable for or refuse RC. However, achieving a satisfactory oncological outcome with TMT requires strict patient selection criteria, and the comparative oncological outcomes of TMT versus RC remain controversial. METHODS: Patients diagnosed with non-metastatic MIBC who underwent TMT or RC were identified from the SEER database during 2004-2015. Before one-to-one propensity score matching (PSM), logistic regression was utilized to identify predictors of TMT. After matching, K-M curves were generated to estimate cancer-specific survival (CSS) and overall survival (OS) with log-rank to test the significance. Finally, we conducted univariate and multivariate Cox analyses to identify independent prognostic factors for CSS and OS. RESULTS: The RC and TMT groups included 5812 and 1260 patients, respectively, and the TMT patients were significantly older than the RC patients. Patients with advanced age, separated, divorced, or widowed (SDW) or unmarried marital status (married as reference), and larger tumor size (< 40 mm as reference) were more likely to be treated with TMT. After PSM, TMT was found to be associated with worse CSS and OS, and it was identified as an independent risk factor for both CSS and OS. CONCLUSION: MIBC patients may not be carefully evaluated prior to TMT, and some non-ideal candidates underwent TMT. TMT resulted in worse CSS and OS in the contemporary era, but these results may be biased. Strict TMT candidate criteria and TMT treatment modality should be required.

Cluster-Based Multinomial Logistic Regression Model for Health-Related Quality of Life Among People Living with HIV in Brazil.

Improvement in treatment options has increased the survival of people living with HIV (PLHIV). Thus, we evaluated the factors associated with better health-related quality of life (HRQoL) among PLHIV in Brazil. This was a cross-sectional study carried out among 349 PLHIV. Data were collected using an interview-based questionnaire, and HRQoL was assessed by the Brazilian version of the WHOQOL HIV BREF instrument. We used non-hierarchical cluster analysis (K-means) to compile the WHOQOL HIV BREF's overall and domain scores into a unique more multidimensional measure for HRQoL consisting of three clusters: poor, fair and good; associations with clusters of better HRQoL were assessed using multinomial logistic regression models. The mean and median overall HRQoL scores were 15.13 (SD = 3.39) and 16, respectively. The reliability and validity of the Brazilian version of the WHOQOL HIV BREF instrument was confirmed among PLHIV in a non-metropolitan, medium-sized municipality of Brazil, which reaffirmed the cross-cultural validity of this instrument. The factors male sex; heterosexual and asexual orientations; higher individual income; undetectable viral load; absence of any comorbidity and presence of an infectious or a chronic comorbidity, with mental illness as the reference; and never having consumed illegal substances were independently associated with good HRQoL. Thus, the compilation of the WHOQOL HIV BREF's overall and domain scores into a unique multidimensional measure for HRQoL, which this study proposed for the first time, may facilitate more robust interpretations and models of predictors. These differentials could simplify HRQoL as an indicator of health and wellbeing to be routinely used as a key outcome in the clinical management of patients and in the global monitoring of health system responses to HIV.

RESUMEN: La mejora en las opciones de tratamiento ha aumentado la supervivencia de las personas que viven con el VIH (PVVIH). Por lo tanto, evaluamos los factores asociados con una mejor calidad de vida relacionada con la salud (CVRS) entre las PVVIH en Brasil. Se trata de un estudio transversal realizado con 349 PVVIH. Los datos se recopilaron mediante un cuestionario basado en entrevistas y la CVRS se evaluó mediante la versión brasileña del instrumento WHOQOL VIH BREF. Usamos un análisis de conglomerados no jerárquico (K-medias) para compilar las puntuaciones generales y de dominios del WHOQOL HIV BREF en una medida única más multidimensional para la CVRS que consta de tres conglomerados: deficiente, regular y bueno; y las asociaciones con grupos de mejor CVRS se evaluaron mediante modelos de regresión logística multinomial. Las puntuaciones de la CVRS global media y mediana fueron 15,13 (DE = 3,39) y 16. La confiabilidad y validez del WHOQOL VIH BREF versión brasileña fue confirmada entre personas que viven con el VIH en un municipio no metropolitano de mediana población de Brasil, lo que reafirma la validez transcultural de este instrumento. Los factores sexo masculino; orientaciones heterosexuales y asexuales; mayores ingresos individuales; carga viral indetectable; ausencia de comorbilidad y presencia de comorbilidad infecciosa o crónica, teniendo como referencia la enfermedad mental; y nunca haber consumido sustancias ilegales se asociaron de forma independiente con una buena CVRS. Por lo tanto, la compilación de las puntuaciones generales y de dominio del WHOQOL HIV BREF en una medida multidimensional única para la CVRS, que este estudio propuso por primera vez, puede facilitar interpretaciones y modelos de predictores más robustos. Estos diferenciales podrían simplificar la HRQoL como un indicador de salud y bienestar para ser utilizado de forma rutinaria como un resultado clave en el manejo clínico de los pacientes y en el monitoreo global de las respuestas del sistema de salud al VIH.

PCA and logistic regression in 2-[18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer's disease.

Objective.to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[18F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer's disease (AD).Approach.as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI).Main results.the best combination of hyperparameters was L1 regularization andC≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD.Significance.our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.