Detecting irregularities in randomized controlled trials using machine learning.

Background: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention.

Learning Curve Analysis for Robotic-assisted Harvest of Deep Inferior Epigastric Perforator Flap.

The deep inferior epigastric perforator (DIEP) flap is the preferred method for autologous breast reconstruction after mastectomy, but risks the development of hernia, bulge, and decreased core strength. Robotic harvest of DIEP vessels may limit abdominal wall morbidity through smaller fascial incisions and preservation of motor nerves. This study shows the expected learning curve (LC) for robotic harvest and compares the LC between a general surgeon (GS) and a plastic surgeon (PS).

Progression to type 1 diabetes in the DPT-1 and TN07 clinical trials is critically associated with specific residues in HLA-DQA1-B1 heterodimers.

Aims/hypothesis: The aim of this work was to explore molecular amino acids (AAs) and related structures of HLA-DQA1-DQB1 that underlie its contribution to the progression from stages 1 or 2 to stage 3 type 1 diabetes.

Methods: Using high-resolution DQA1 and DQB1 genotypes from 1216 participants in the Diabetes Prevention Trial-Type 1 and the Diabetes Prevention Trial, we applied hierarchically organised haplotype association analysis (HOH) to decipher which AAs contributed to the associations of DQ with disease and their structural properties. HOH relied on the Cox regression to quantify the association of DQ with time-to-onset of type 1 diabetes.

Cancer researchers as storytellers for the lay public.

Cancer researchers tend to be well-versed in communicating research and research results to scientific audiences. To maintain momentum and progress against cancer, they must acquire and nurture skills allowing for better engagement with the lay public.

Menstrual cycle phase differences in myofiber damage and macrophage infiltration following electrical stimulation-induced muscle injury.

The purpose of this study was to examine the effects of menstrual cycle phase on myofiber injury, regenerative events, and inflammation after electrical stimulation (ES)-induced myofiber damage. Twenty-eight premenopausal women (20.8 ± 2 yr) were randomized into early follicular (EF; = 14) and late follicular (LF; = 14) groups.