Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

An Introduction to Factored Regression Models with Blimp

In this paper, we provide an introduction to the factored regression framework. This modeling framework applies the rules of probability to break up or “factor” a complex joint distribution into a product of conditional regression models. Using this framework, we can easily specify the complex multivariate models that missing data modeling requires. The article provides a brief conceptual overview of factored regression and describes the functional notation used to conceptualize the models. Furthermore, we present a conceptual overview of how the models are estimated and imputations are obtained. Finally, we discuss how users can use the free software package, Blimp, to estimate the models in the context of a mediation example.

Gender and Racial Gaps in Support for Policing and Correctional Reforms: Are the Gaps a Consequence of Political Partisanship?

Divisive criminal justice issues are typically framed through gender and racial lenses, with little empirical work considering the increasing role of political partisanship. Using the 2016 Cooperative Congressional Election Study ( N = 55,000), we estimate multivariate models of support for four policing and correctional reforms. The models initially point to gender gaps and racial gaps. However, as with many public policy issues, support for criminal justice reforms are largely a product of political partisanship—the gender and racial gaps are largely a consequence of gender and racial gaps in partisanship and appear to be driven by white Republican men. As legislative bodies continue to be overrepresented with individuals with the same demographic profile, criminal justice reform prospects are limited.

Composite Scoring System and Optimal Tumor Budding Cut-Off Number for Estimating Lymph Node Metastasis in Submucosal Colorectal Cancer

Background Tumor budding is associated with lymph node (LN) metastasis in submucosal colorectal cancer (CRC). However, the rate of LN metastasis associated with the number of tumor buds is unknown. Here, we determined the optimal tumor budding cut-off number and developed a composite scoring system (CSS) for estimating LN metastasis of submucosal CRC. Methods In total, 395 patients with histologically confirmed T1N0–2M0 CRC were evaluated. The clinicopathological characteristics were subjected to univariate and multivariate analyses. The Akaike information criterion (AIC) values of the multivariate models were evaluated to identify the optimal cut-off number. A CSS for LN metastasis was developed using independent risk factors. Results The prevalence of LN metastasis was 13.2%. Histological differentiation, lymphatic or venous invasion, and tumor budding were associated with LN metastasis in univariate analyses. In multivariate models adjusted for histological differentiation and lymphatic or venous invasion, the AIC value was lowest for five tumor buds. Unfavorable differentiation (odds ratio [OR], 8.16; 95% confidence interval [CI], 1.80–36.89), lymphatic or venous invasion (OR, 5.91; 95% CI, 2.91–11.97), and five or more tumor buds (OR, 3.01; 95% CI, 1.21–7.69) were independent risk factors. In a CSS using these three risk factors, the rates of LN metastasis were 5.6%, 15.5%, 31.0%, and 52.4% for total composite scores of 0, 1, 2, and ≥ 3, respectively. Conclusions For the estimation of LN metastasis in submucosal CRC, the optimal tumor budding cut-off number was five. Our CSS can be utilized to estimate LN metastasis.

Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer

Introduction Currently, the accurate evaluation and prediction of response to neoadjuvant chemotherapy (NAC) remains a great challenge. We developed several multivariate models based on baseline imaging features and clinicopathological characteristics to predict the breast pathologic complete response (pCR). Methods We retrospectively collected clinicopathological and imaging data of patients who received NAC and subsequent surgery for breast cancer at our hospital from 2014 June till 2020 September. We used mammography, ultrasound and magnetic resonance imaging (MRI) to investigate the breast tumors at baseline. Results A total of 308 patients were included and 111 patients achieved pCR. The HER2 status and Ki-67 index were significant factors for pCR on univariate analysis and in all multivariate models. Among the prediction models in this study, the ultrasound-MRI model performed the best, producing an area under curve of 0.801 (95%CI=0.749-0.852), a sensitivity of 0.797 and a specificity of 0.676. Conclusion Among the multivariable models constructed in this study, the ultrasound plus MRI model performed the best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.

Analysing the Protein-DNA Binding Sites in Arabidopsis thaliana from ChIP-seq Experiments

Computational genomics aim at supporting the discovery of how the functionality of the genome of the organism under study is affected both by its own sequence and structure, and by the network of interaction between this genome and different biological or physical factors. In this work, we focus on the analysis of ChIP-seq data, for which many methods have been proposed in the recent years. However, to the best of our knowledge, those methods lack an appropriate mathematical formalism. We have developed a method based on multivariate models for the analysis of the set of peaks obtained from a ChIP-seq experiment. This method can be used to characterize an individual experiment and to compare different experiments regardless of where and when they were conducted. The method is based on a multivariate hypergeometric distribution, which fits the complexity of the biological data and is better suited to deal with the uncertainty generated in this type of experiments than the dichotomous models used by the state of the art methods. We have validated this method with Arabidopsis thaliana datasets obtained from the Remap2020 database, obtaining results in accordance with the original study of these samples. Our work shows a novel way for analyzing ChIP-seq data.

Modeling Coronavirus Pandemic Using Univariate and Multivariate Models: The Nigerian Perspective

Corona virus Disease, a disease which was discovered in December, 2019 has been spreading worldwide like wildfire. In view of this, there is need of continuous findings on the impact, consequence and possible medications of the pandemic in Nigeria and the world at large. Therefore, this research is aimed at Analyzing the spread of Coronavirus pandemic in Nigeria, using univariate and multivariate models namely;(ARIMA) and (ARIMAX). The daily data used in this research was obtained from the NCDC official website dated from 19th April, 2020 to 20th April, 2021 with total of 384 observations using R and Eview10 software for the analysis. Three different variables were examined. The variables are; total confirmed, discharged and death cases for the purpose of establishing reliable forecast, for better decision making and a helping technique for drastic action in reducing the day to day spread of the pandemic. Summary statistics and stationary test were checked with the data being stationary at the first difference and design technique was conducted as well. Also, best fitted model was selected using Akaike Information Criteria (AIC). The ARIMA (1,1,3) model with an exogenous variable was chosen from the ARIMA models with minimum AIC. From the model, a prediction of sixty-days forecast showed the upward trend of the total confirmed cases of the pandemic in the country. The government on its part via its task force can use the predicted line to take much necessary measures and emphases on taking COVID-19 vaccines so as to prevent further spread of the virus

Childhood temperament as a predictor of adolescent nonsuicidal self-injury

Previous cross-sectional work has consistently found associations between neuroticism and impulsivity and nonsuicidal self-injury (NSSI). However, there are few longitudinal studies of personality risk factors for NSSI. In this study, we examined associations between individual differences in temperament at age 3 and NSSI from ages 9 to 15. At age 3, 559 preschool-aged children (54% male; Mage = 42.2 months [SD = 3.10]) completed laboratory assessments of temperament. Parents also completed questionnaires about their child’s temperament. Children completed a diagnostic interview assessing NSSI engagement at ages 9, 12, and 15. By the age 15 assessment, 12.4% of adolescents reported engaging in NSSI. In univariate models, we found that higher levels of observed sadness and maternal-reported sadness and anger were associated with increased risk for NSSI. In multivariate models, female sex and maternal-reported anger were significantly associated with greater likelihood of NSSI. Laboratory observed sadness and impulsivity were associated with a higher likelihood of NSSI. This work extends the literature on personality risk factors associated with NSSI by finding longitudinal associations between early childhood negative affect and later NSSI engagement during adolescence.

Telehealth and 30-Day Readmissions Among Heart Failure Patients During the COVID-19 Pandemic

This study examined whether outpatient follow-up within 14 days of discharge via telehealth visits are as effective as in-person visits for reducing 30-day readmission in heart failure (HF) patients. Using electronic health records from a large health system, we included HF patients (n=1,722) who were hospitalized during the period of March 15-July 15, 2020. Overall, 28.1% of patients received an early outpatient follow-up visit. Patients who received telehealth visits (n=119) were more likely to be older and live in areas with higher median household incomes than those with in-person visits (n=365). Thirty-day readmission rates were 20.5% during the COVID-19 period. Multivariate models showed that patients who received a telehealth (OR=0.36, 95%CI [0.23-0.56]) or an in-person (OR=0.42, 95%CI [0.31-0.57]) visit were less likely to be readmitted within 30 days compared with patients without an early outpatient follow-up. Telehealth visits were just as effective as in-person visits at reducing 30-day readmissions.

Associations of cardiovascular diseases and type 2 diabetes with ophthalmic diseases in a population sample over 55 years old

Aim. To study associations of cardiovascular diseases and type 2 diabetes (T2D) with ophthalmic diseases in a population sample of men and women from middle to old age (Novosibirsk).Material and methods. The population cohort was initially studied in 2003-2005 (n=9360, 45-69 years old, Novosibirsk, the Health, Alcohol and Psychosocial factors in Eastern Europe (HAPIEE) project). At the second survey (2015-2017) in a random subsample (n=1011), the following ophthalmic diseases were identified: hypertensive retinopathy (HR), diabetic retinopathy (DR), cataract, glaucoma, age-related macular degeneration (AMD), optic disc abnormalities, etc.Results. The prevalence of HR signs in persons with and without hypertension (HTN) was 81 and 46%, respectively (p<0,001). This association persisted regardless of other factors (odds ratio, 2,27 (95% confidence interval: 1,78-4,17). The prevalence of AMD, cataract and DR increased in HTN, but associations were largely explained by metabolic factors in multivariate models. People with T2D more often than without T2D had signs of DR (9,3 vs 0,4%, p<0,001), AMD (22 vs 17%, p=0,042) and glaucoma (14 vs 7%, p=0,001). Associations of T2D with DR and glaucoma persisted regardless of other factors. Individuals with carotid atherosclerosis (CA) were 1,6 times more likely to have HR than those without CA when adjusted for sex, age, and smoking (p=0,013).Conclusion. In the surveyed population sample of mainly elderly people, a number of associations between cardiometabolic and common ophthalmic diseases were revealed. The identified comorbidities may have important therapeutic and prophylactic applications in an aging population.