Hmong Adults Self-Rated Oral Health: A Pilot Study

Since 1975, the Hmong refugee population in the U.S. has increased over 200%. However, little is known about their dental needs or self-rated oral health (SROH). The study aims were to: (1) describe the SROH, self-rated general health (SRGH), and use of dental/physician services; and (2) identify the factors associated with SROH among Hmong adults. A cross-sectional study design with locating sampling methodology was used. Oral health questionnaire was administered to assess SROH and SRGH, past dental and physician visits, and language preference. One hundred twenty adults aged 18–50+ were recruited and 118 had useable information. Of these, 49% rated their oral health as poor/fair and 30% rated their general health as poor/fair. Thirty-nine percent reported that they did not have a regular source of dental care, 46% rated their access to dental care as poor/fair, 43% visited a dentist and 66% visited a physician within the past 12 months. Bivariate analyses demonstrated that access to dental care, past dental visits, age and SRGH were significantly associated with SROH (P \u3c 0.05). Multivariate analyses demonstrated a strong association between access to dental care and good/excellent SROH. About half of Hmong adults rated their oral health and access to dental care as poor. Dental insurance, access to dental care, past preventive dental/physician visits and SRGH were associated with SROH

Further evidence for a variable fine-structure constant from Keck/HIRES QSO absorption spectra

[Abridged] We previously presented evidence for a varying fine-structure constant, alpha, in two independent samples of Keck/HIRES QSO spectra. Here we present a detailed many-multiplet analysis of a third Keck/HIRES sample containing 78 absorption systems. We also re-analyse the previous samples, providing a total of 128 absorption systems over the redshift range 0.2<z_abs<3.7. All three samples separately yield consistent, significant values of da/a. The analyses of low- and high-z systems rely on different ions/transitions with very different dependencies on alpha, yet they also give consistent results. We identify additional random errors in 22 high-z systems characterized by transitions with a large dynamic range in apparent optical depth. Increasing the statistical errors on da/a for these systems gives our fiducial result, a weighted mean da/a=(-0.543+/-0.116)x10^-5, representing 4.7-sigma evidence for a smaller weighted mean alpha in the absorption clouds. Assuming that da/a=0 at z_abs=0, the data marginally prefer a linear increase in alpha with time: dota/a=(6.40+/-1.35)x10^-16 yr^-1. The two-point correlation function for alpha is consistent with zero over 0.2-13 Gpc comoving scales and the angular distribution of da/a shows no significant dipolar anisotropy. We therefore have no evidence for spatial variations in da/a. We extend our previous searches for possible systematic errors, identifying atmospheric dispersion and isotopic structure effects as potentially the most significant. However, overall, known systematic errors do not explain the results. Future many-multiplet analyses of QSO spectra from different telescopes and spectrographs will provide a now crucial check on our Keck/HIRES results.Comment: 31 pages, 25 figures (29 EPS files), 8 tables. Accepted by MNRAS. Colour versions of Figs. 6, 8 & 10 and text version of Table 3 available at http://www.ast.cam.ac.uk/~mim/pub.htm

International Validation of a Nomogram to Predict Recurrence after Resection of Grade 1 and 2 Nonfunctioning Pancreatic Neuroendocrine Tumors

Background: Despite the low recurrence rate of resected nonfunctional pancreatic neuroendocrine tumors (NF-pNETs), nearly all patients undergo long-term surveillance. A prediction model for recurrence may help select patients for less intensive surveillance or identify patients for adjuvant therapy. The objective of this study was to assess the external validity of a recently published model predicting recurrence within 5 years after surgery for NF-pNET in an international cohort. This prediction model includes tumor grade, lymph node status and perineural invasion as predictors. Methods: Retrospectively, data were collected from 7 international referral centers on patients who underwent resection for a grade 1-2 NF-pNET between 1992 and 2018. Model performance was evaluated by calibration statistics, Harrel's C-statistic, and area under the curve (AUC) of the receiver operating characteristic curve for 5-year recurrence-free survival (RFS). A sub-analysis was performed in pNETs >2 cm. The model was improved to stratify patients into 3 risk groups (low, medium, high) for recurrence. Results: Overall, 342 patients were included in the validation cohort with a 5-year RFS of 83% (95% confidence interval [CI]: 78-88%). Fifty-eight patients (17%) developed a recurrence. Calibration showed an intercept of 0 and a slope of 0.74. The C-statistic was 0.77 (95% CI: 0.70-0.83), and the AUC for the prediction of 5-year RFS was 0.74. The prediction model had a better performance in tumors >2 cm (C-statistic 0.80). Conclusions: External validity of this prediction model for recurrence after curative surgery for grade 1-2 NF-pNET showed accurate overall performance using 3 easily accessible parameters. This model is available via www.pancreascalculator.com

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients