Gut-brain connection affects overall health

Medical and dietetic students often co-author a column for the Daily Reflector under Dr. Kolasa's byline. The students research the topic a reader or patient has asked. Dr. Kolasa reviews their draft for technical accuracy, patient friendly language, people first language. She fact checks the study or other evidence-based reference the student provides. If a physician review is appropriate, Dr. Kolasa requests a colleague from ECU physicians to review the article. The final draft is submitted to the Reflector with the editor having the final say. The headline is written by the Reflector headline writer. The food and nutrition column has run weekly since 1987. Starting in 2020, in addition to the Daily Reflector, the article is published in daily and weekly papers owned by the Adams Publishing Group East (https://adamspg.com)This is a weekly Q and A newspaper column under the byline of Dr. Kathy Kolasa. Today's column is discussing the topic of the gut-brain axis, and how our nutrition can affect its health.Non

Feasibility of Patient Reporting of Symptomatic Adverse Events via the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PROCTCAE) in a Chemoradiotherapy Cooperative Group Multicenter Clinical Trial

Purpose—To assess the feasibility of measuring symptomatic adverse events (AEs) in a multicenter clinical trial using the National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Methods and Materials—Patients enrolled in Trial XXXX (XXXX) were asked to self-report 53 PRO-CTCAE items representing 30 symptomatic AEs at 6 time points (baseline; weekly x4 during treatment; 12-weeks post-treatment). Reporting was conducted via wireless tablet computers in clinic waiting areas. Compliance was defined as the proportion of visits when an expected PRO-CTCAE assessment was completed. Results—Among 226 study sites participating in Trial XXXX, 100% completed 35-minute PROCTCAE training for clinical research associates (CRAs); 80 sites enrolled patients of which 34 (43%) required tablet computers to be provided. All 152 patients in Trial XXXX agreed to selfreport using the PRO-CTCAE (median age 66; 47% female; 84% white). Median time for CRAs to learn the system was 60 minutes (range 30–240), and median time for CRAs to teach a patient to self-report was 10 minutes (range 2–60). Compliance was high, particularly during active treatment when patients self-reported at 86% of expected time points, although compliance was lower post-treatment (72%). Common reasons for non-compliance were institutional errors such as forgetting to provide computers to participants; patients missing clinic visits; internet connectivity; and patients feeling “too sick”. Conclusions—Most patients enrolled in a multicenter chemoradiotherapy trial were willing and able to self-report symptomatic adverse events at visits using tablet computers. Minimal effort was required by local site staff to support this system. The observed causes of missing data may be obviated by allowing patients to self-report electronically between-visits, and by employing central compliance monitoring. These approaches are being incorporated into ongoing studies

Universal promoter scanning by Pol II during transcription initiation in Saccharomyces cerevisiae

Abstract Background The majority of eukaryotic promoters utilize multiple transcription start sites (TSSs). How multiple TSSs are specified at individual promoters across eukaryotes is not understood for most species. In Saccharomyces cerevisiae, a pre-initiation complex (PIC) comprised of Pol II and conserved general transcription factors (GTFs) assembles and opens DNA upstream of TSSs. Evidence from model promoters indicates that the PIC scans from upstream to downstream to identify TSSs. Prior results suggest that TSS distributions at promoters where scanning occurs shift in a polar fashion upon alteration in Pol II catalytic activity or GTF function. Results To determine the extent of promoter scanning across promoter classes in S. cerevisiae, we perturb Pol II catalytic activity and GTF function and analyze their effects on TSS usage genome-wide. We find that alterations to Pol II, TFIIB, or TFIIF function widely alter the initiation landscape consistent with promoter scanning operating at all yeast promoters, regardless of promoter class. Promoter architecture, however, can determine the extent of promoter sensitivity to altered Pol II activity in ways that are predicted by a scanning model. Conclusions Our observations coupled with previous data validate key predictions of the scanning model for Pol II initiation in yeast, which we term the shooting gallery. In this model, Pol II catalytic activity and the rate and processivity of Pol II scanning together with promoter sequence determine the distribution of TSSs and their usage.http://deepblue.lib.umich.edu/bitstream/2027.42/173852/1/13059_2020_Article_2040.pd

Validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on L3 abdominal CT images

Background: body composition assessment using abdominal computed tomography (CT) images is increasingly applied in clinical and translational research. Manual segmentation of body compartments on L3 CT images is time-consuming and requires significant expertise. Robust high-throughput automated segmentation is key to assess large patient cohorts and ultimately, to support implementation into routine clinical practice. By training a deep learning neural network (DLNN) with several large trial cohorts and performing external validation on a large independent cohort, we aim to demonstrate the robust performance of our automatic body composition segmentation tool for future use in patients.Methods: L3 CT images and expert-drawn segmentations of skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue of patients undergoing abdominal surgery were pooled (n = 3,187) to train a DLNN. The trained DLNN was then externally validated in a cohort with L3 CT images of patients with abdominal cancer (n = 2,535). Geometric agreement between automatic and manual segmentations was evaluated by computing two-dimensional Dice Similarity (DS). Agreement between manual and automatic annotations were quantitatively evaluated in the test set using Lin’s Concordance Correlation Coefficient (CCC) and Bland-Altman’s Limits of Agreement (LoA).Results: the DLNN showed rapid improvement within the first 10,000 training steps and stopped improving after 38,000 steps. There was a strong concordance between automatic and manual segmentations with median DS for skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: skeletal muscle 0.964 (0.959-0.968), visceral adipose tissue 0.998 (0.998-0.998), and subcutaneous adipose tissue 0.992 (0.991-0.993). Bland-Altman metrics (relative to approximate median values in parentheses) indicated only small and clinically insignificant systematic offsets : 0.23 HU (0.5%), 1.26 cm2.m-2 (2.8%), -1.02 cm2.m-2 (1.7%), and 3.24 cm2.m-2 (4.6%) for skeletal muscle average radiodensity, skeletal muscle index, visceral adipose tissue index, and subcutaneous adipose tissue index, respectively. Assuming the decision thresholds by Martin et al. for sarcopenia and low muscle radiation attenuation, results for sensitivity (0.99 and 0.98 respectively), specificity (0.87 and 0.98 respectively), and overall accuracy (0.93) were all excellent.Conclusion: we developed and validated a deep learning model for automated analysis of body composition of patients with cancer. Due to the design of the DLNN, it can be easily implemented in various clinical infrastructures and used by other research groups to assess cancer patient cohorts or develop new models in other fields