The early detection of cancer can reduce cancer-related mortality. There is no clinically useful noninvasive biomarker for early detection of breast cancer. The aim of this study was to develop accurate and precise early detection biomarkers and a dynamic monitoring system following treatment. We analyzed a genome-wide methylation array in Taiwanese and The Cancer Genome Atlas (TCGA) breast cancer (BC) patients. Most breast cancer-specific circulating methylated CCDC181, GCM2 and ITPRIPL1 biomarkers were found in the plasma. An automatic analysis process of methylated ccfDNA was established. A combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was performed in R using Recursive Partitioning and Regression Trees to establish a new prediction model. Combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was found to have a sensitivity level of 97% and an area under the curve (AUC) of 0.955 in the training set, and a sensitivity level of 100% and an AUC of 0.961 in the test set. The circulating methylated CCDC181, GCM2 and ITPRIPL1 was also significantly decreased after surgery (all p < 0.001). The aberrant methylation patterns of the CCDC181, GCM2 and ITPRIPL1 genes means that they are potential biomarkers for the detection of early BC and can be combined with breast imaging data to achieve higher accuracy, sensitivity and specificity, facilitating breast cancer detection. They may also be applied to monitor the surgical treatment response.

Background: Studies have demonstrated that health care students and practitioners are not immune to stigma towards people living with HIV (PLHIV). This attitude could lead to poor quality of care if it remains uncorrected. However, little is known about dietetic students’ acceptance of PLHIV despite their substantial role in treatment. This study aimed to measure the extent of knowledge and stigma towards PLHIV among dietetic students and to determine the associated factors using the attribution theory.Methods: Students from three dietetics schools in Indonesia (n = 516) were recruited to participate in this cross-sectional study. Survey questions covered demographic information, interaction with PLHIV, access to information sources, cultural values, and beliefs as predictor variables. The outcome variables were comprehensive knowledge of HIV, HIV and nutrition-specific knowledge, and attitudes. Analyses with linear regression and the stepwise selection were performed to determine factors related to the outcome.Results: The levels of HIV comprehensive knowledge and HIV-nutrition specific knowledge among dietetic students were low, as indicated by the average score of 19.9 ± 0.19 (maximum score = 35) and 8.0 ± 0.11 (maximum score = 15), respectively. The level of negative attitudes towards PLHIV was high, with 99.6% of participants reported having a high stigma score. Types of university affiliation (public or private), beliefs and values, exposure to HIV discourse, access to printed media, and years of study were significantly related to HIV comprehensive knowledge (p < 0.05). Nutrition-specific knowledge was also correlated with university affiliation, beliefs and values, participation in HIV discussion, and years of study (p < 0.05). HIV comprehensive knowledge, university affiliation, discussion participation, and ethnicities were associated with attitudes (p < 0.05).Conclusions: Awareness and acceptance of PLHIV must be further improved throughout dietetic training to ensure patients’ quality of care since students represent future dietary care providers. Considering the consistent findings that affiliation to education institution correlates with HIV knowledge and attitude, some examinations concerning the curriculum and teaching conduct might be necessary.

Focal Therapy (FT) is an emerging treatment modality for prostate cancer (PCa). Due to its novelty, the research exploring how patients should be followed-up after treatment is limited. There is currently no established role for non-prostate-specific-antigen (PSA) biomarkers and PSMA PET. However, a combination of PSA testing, multiparametric magnetic resonance imaging (mpMRI), and systematic and targeted biopsies should routinely be used for surveillance after FT. PSA values that rise 1.0 ng/mL over the nadir after twelve months or rise 1.5 ng/mL over nadir after twenty-four to thirty-six months should raise suspicion for recurrence. The standard imaging technique is mpMRI, but it can often be difficult to interpret after FT, so using a scoring system such as prostate imaging after focal ablation (PI-FAB) or the transatlantic recommendations for prostate gland evaluation with magnetic resonance imaging after focal therapy (TARGET) allows for greater consistency between readers. This review seeks to summarize the current literature regarding surveillance after FT as it relates to biomarkers, imaging, biopsies, and consensus statements.

Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high‐dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high‐dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high‐dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.

Tests of homogeneity in zero-inflated models for count data have been well discussed in the literature, but the existing methodologies have relied primarily on score statistics. An often cited justification for the use of these statistics is that they do not require the model to be fitted under the alternative. But the advent of computer software with robust functions and procedures has made it easy for these alternative models to be fitted routinely in practice. In this article, we exploit this opportunity by using results generated from these analyses to develop a Wald test statistic to evaluate homogeneity in the class of zero-inflated models. We show how the proposed test can be performed with minimal programming effort for the practicing statistician. Technically, the test is based on a reparameterization of the mixing weights that naturally incorporates covariates under heterogeneity, a characteristic often ignored by existing testing procedures. A quasi-likelihood function derived from a working independence model coupled with the so-called sandwich estimator of the covariance matrix of the parameter estimates is used to accommodate correlation in the data. A simulation study is conducted to evaluate the empirical performance of the proposed Wald test and its real life applications are illustrated using correlated dental caries counts in young children.