Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC.
 
  The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes.
 
  A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC.  https://www.selleckchem.com/products/evobrutinib.html  Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues.
 
  The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.
 The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.
  To identify factors that influence or interfere with referrals by primary care providers (PCPs) to a pharmacist-led telephone-based program to assist patients undergoing opioid tapering. The Support Team Onsite Resource for Management of Pain (STORM) program provides individualized patient care and supports PCPs in managing opioid tapers.
 
  Qualitative interviews were conducted with referring PCPs and STORM staff. Interview guides addressed concepts from the RE-AIM framework, focusing on issues affecting referral to the STORM program.
 
  An integrated healthcare system (HCS) in the Northwest United States.
 
  Thirty-five interviews were conducted with 20 PCPs and 15 STORM staff.
 
  Constant comparative analysis was used to identify key themes from interviews. A codebook was developed based on interview data and a qualitative software program was used for coding, iterative review, and content analysis. Representative quotes illustrate identified themes.
 
  Use of the STORM opioid tapering program was influenced by PCP, patient, and HCS considerations. Factors motivating use of STORM included lack of PCP time to support chronic pain patients requiring opioid tapering and the perception that STORM is a valued partner in patient care. Impediments to referral included PCP confidence in managing opioid tapering, patient resistance to tapering, forgetting about program availability, and PCP resistance to evolving guidelines regarding opioid tapering goals.
 
  PCPs recognized that STORM supported patient safety and reduced clinician burden. Utilization of the program could be improved through ongoing PCP education about the service and consistent co-location of STORM pharmacists within primary care clinics.
 PCPs recognized that STORM supported patient safety and reduced clinician burden. Utilization of the program could be improved through ongoing PCP education about the service and consistent co-location of STORM pharmacists within primary care clinics.When validating a risk model in an independent cohort, some predictors may be missing for some subjects. Missingness can be unplanned or by design, as in case-cohort or nested case-control studies, in which some covariates are measured only in subsampled subjects. Weighting methods and imputation are used to handle missing data. We propose methods to increase the efficiency of weighting to assess calibration of a risk model (i.e. bias in model predictions), which is quantified by the ratio of the number of observed events, $\mathcalO$, to expected events, $\mathcalE$, computed from the model. We adjust known inverse probability weights by incorporating auxiliary information available for all cohort members. We use survey calibration that requires the weighted sum of the auxiliary statistics in the complete data subset to equal their sum in the full cohort. We show that a pseudo-risk estimate that approximates the actual risk value but uses only variables available for the entire cohort is an excellent auxiliary statistic to estimate $\mathcalE$. We derive analytic variance formulas for $\mathcalO/\mathcalE$ with adjusted weights. In simulations, weight adjustment with pseudo-risk was much more efficient than inverse probability weighting and yielded consistent estimates even when the pseudo-risk was a poor approximation. Multiple imputation was often efficient but yielded biased estimates when the imputation model was misspecified. Using these methods, we assessed calibration of an absolute risk model for second primary thyroid cancer in an independent cohort.Hepatocytes are essential for maintaining the homeostasis of iron and lipid metabolism in mammals. Dysregulation of either iron or lipids has been linked with serious health consequences, including non-alcoholic fatty liver disease (NAFLD). Considered the hepatic manifestation of metabolic syndrome, NAFLD is characterised by dysregulated lipid metabolism leading to a lipid storage phenotype. Mild to moderate increases in hepatic iron have been observed in ∼30% of individuals with NAFLD; however, direct observation of the mechanism behind this increase has remained elusive. To address this issue, we sought to determine the metabolic consequences of iron loading on cellular metabolism using live cell, time-lapse Fourier transform infrared (FTIR) microscopy utilising a synchrotron radiation source to track biochemical changes. The use of synchrotron FTIR is non-destructive and label-free, and allowed observation of spatially resolved, sub-cellular biochemical changes over a period of 8 h. Using this approach, we have demonstrated that iron loading in AML12 cells induced perturbation of lipid metabolism congruent with steatosis development.