Key Points Question Are legal mandates for naloxone coprescription associated with increases in naloxone prescription dispensing? Findings In this population-based, state-level cohort study using data from retail pharmacies in all 50 states and the District of Columbia, having a legal mandate for naloxone coprescription was associated with approximately 7.75 times more dispensed naloxone prescriptions compared with not having the requirements. This equates to an estimated 214 additional naloxone prescriptions dispensed per month in the period following the mandates, holding all other variables constant. Meaning State legal interventions that mandate naloxone coprescription for potentially at-risk patients may be associated with increases in naloxone prescription dispensing in retail pharmacies, and this strategy may be useful to improve naloxone availability and reduce opioid-related harm.
Over the past 20 years, various identifiers of cellular senescence have been used to quantify the abundance of these cells in different tissues. These include classic markers such as p16, senescence-associated β-gal, and γH2AX, in addition to more recent markers (Sudan Black B and HMGB1). In vivo data on the usefulness of these markers in skeletal muscle are very limited and inconsistent. In the present study, we attempted to identify senescent cells in frozen human skeletal muscle biopsies using these markers to determine the effects of age and obesity on senescent cell burden; however, we were only able to assess the abundance of DNA-damaged nuclei using γH2AX immunohistochemistry. The abundance of γH2AX+ cells, including satellite cells, was not higher in muscle from old compared to young individuals; however, γH2AX+ cells were higher with obesity. Additionally, terminally differentiated, postmitotic myofiber nuclei from obese individuals had elevated γH2AX abundance compared to muscle from lean individuals. Analyses of gene expression support the conclusion that the elevated DNA damage and the senescence-associated secretory phenotype are preferentially associated with obesity in skeletal muscle. These data implicate obesity as a larger contributor to DNA damage in skeletal muscle than aging; however, more sensitive senescence markers for human skeletal muscle are needed to determine if these cells are in fact senescent. K E Y W O R D SDNA damage, postmitotic, satellite cells, senescence, γH2AX | 7019 DUNGAN et Al.
Background: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are two primary subtypes of non-small cell lung carcinoma (NSCLC). Currently, the most widely used method to discriminate between LUAD and LUSC is hematoxylin-eosin (HE) staining. However, this method sometimes is unable to make the precise diagnosis on LUAD or LUSC. More accurate diagnostic approaches are highly desired. Methods: We propose to use gene expression profile to discriminate NSCLC patient's subtype. We leveraged RNA-Seq data from The Cancer Genome Atlas (TCGA) and randomly split the data into training and testing subsets. To construct classifiers based on the training data, we considered three methods: logistic regression on principal components (PCR), logistic regression with LASSO shrinkage (LASSO), and kth nearest neighbors (KNN). Performances of classifiers were evaluated and compared based on the testing data. Results: All gene expression-based classifiers show high accuracy in discriminating LUSC and LUAD. The classifier obtained by LASSO has the smallest overall misclassification rate of 3.42% (95% CI: 3.25%-3.60%) when using 0.5 as the cutoff value for the predicted probability of belonging to a subtype, followed by classifiers obtained by PCR (4.36%, 95% CI: 4.23%-4.49%) and KNN (8.70%, 95% CI: 8.57%-8.83%). The LASSO classifier also has the highest average area under the receiver operating characteristic curve (AUC) value of 0.993, compared to PCR (0.987) and KNN (0.965). Conclusions: Our results suggest that mRNA expressions are highly informative for classifying NSCLC subtypes and may potentially be used to assist clinical diagnosis.
Mass spectrometry (MS) is frequently used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs). Zero values can be further grouped into biological PMVs and technical PMVs. The former type is caused by true absence of a compound and the later type is caused by a technical detection limit. Methods based on a mixture model have been developed to separate the two types of zeros and to perform differential abundance analysis comparing proteomic/metabolomic profiles between different groups of subjects. However, we notice that those methods may give unstable estimate of the model variance, and thus lead to false positive and false negative results when the number of non-zero values is small. In this paper, we propose a new differential abundance analysis method, DASEV, which uses an empirical Bayes shrinkage method to more robustly estimate the variance and enhance the accuracy of differential abundance analysis. Simulation studies and real data analysis show that DASEV substantially improves parameter estimation of the mixture model and outperforms current methods in identifying differentially abundant features.
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