Description: This work presents a digital microfluidic platform called HYPER-Melt (high-density profiling and enumeration by melt) for highly parallelized copy-by-copy DNA molecular profiling. HYPER-Melt provides a facile means of detecting and assessing sequence variations of thousands of individual DNA molecules through digitization in a nanowell microchip array, allowing amplification and interrogation of individual template molecules by detecting HRM fluorescence changes due to sequence-dependent denaturation. As a model application, HYPER-Melt is used here for the detection and assessment of intermolecular heterogeneity of DNA methylation within the promoters of classical tumor suppressor genes. The capabilities of this platform are validated through serial dilutions of mixed epialleles, with demonstrated detection limits as low as 1 methylated variant in 2 million unmethylated templates (0.00005%) of a classic tumor suppressor gene, CDKN2A (p14 ARF ). The clinical potential of the platform is demonstrated using a digital assay for NDRG4 , a tumor suppressor gene that is commonly methylated in colorectal cancer, in liquid biopsies of healthy and colorectal cancer patients. Overall, the platform provides the depth of information, simplicity of use, and single-molecule sensitivity necessary for rapid assessment of intermolecular variation contributing to genetic and epigenetic heterogeneity for challenging applications in embryogenesis, carcinogenesis, and rare biomarker detection.

Description: Objectives We examine interactions among 3 factors that affect patient waits and use of overtime in outpatient clinics: clinic congestion, patient punctuality and physician processing rates. We hypothesise that the first 2 factors affect physician processing rates, and this adaptive physician behaviour serves to reduce waiting times and the use of overtime. Setting 2 urban academic clinics and an affiliated suburban clinic in metropolitan Baltimore, Maryland, USA. Participants Appointment times, patient arrival times, start of service and physician processing times were collected for 105 visits at a low-volume suburban clinic 1, 264 visits at a medium-volume academic clinic 2 and 22 266 visits at a high-volume academic clinic 3 over 3 distinct spans of time. Intervention Data from the first clinic were previously used to document an intervention to influence patient punctuality. This included a policy that tardy patients were rescheduled. Primary and secondary outcome measures Clinicians' processing times were gathered, conditioned on whether the patient or clinician was tardy to test the first hypothesis. Probability distributions of patient unpunctuality were developed preintervention and postintervention for the clinic in which the intervention took place and these data were used to seed a discrete-event simulation. Results Average physician processing times differ conditioned on tardiness at clinic 1 with p=0.03, at clinic 2 with p=10 –5 and at clinic 3 with p=10 –7 . Within the simulation, the adaptive physician behaviour degrades system performance by increasing waiting times, probability of overtime and the average amount of overtime used. Each of these changes is significant at the p〈0.01 level. Conclusions Processing times differed for patients in different states in all 3 settings studied. When present, this can be verified using data commonly collected. Ignoring these behaviours leads to faulty conclusions about the efficacy of efforts to improve clinic flow.