Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity.

A machine learning case study to predict rare clinical event of interest: imbalanced data, interpretability, and practical considerations.

Accurate prediction of a rare and clinically important event following study treatment has been crucial in drug development. For instance, the rarity of an adverse event is often commensurate with the seriousness of medical consequences, and delayed detection of the rare adverse event can pose significant or even life-threatening health risks to patients. In this machine learning case study, we demonstrate with an example originated from a real clinical trial setting how to define and solve the rare clinical event prediction problem using machine learning in pharmaceutical industry.

Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists.

Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification.

Enrollment forecast for clinical trials at the portfolio planning phase based on site-level historical data.

An accurate forecast of a clinical trial enrollment timeline at the planning phase is of great importance to both corporate strategic planning and trial operational excellence. The naive approach often calculates an average enrollment rate from historical data and generates an inaccurate prediction based on a linear trend with the average rate. Under the traditional framework of a Poisson-Gamma model, site activation delays are often modeled with either fixed initiation time or a simple random distribution while incorporating the user-provided site planning information to achieve good forecast accuracy.

Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data.

Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naïve approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using time-invariant rates for site activation and subject recruitment. Both of them are lack of non-trivial factors such as time and location.

Dehydration of Ions in Voltage-Gated Carbon Nanopores Observed by in Situ NMR.

Ion transport through nanochannels is of fundamental importance in voltage-gated protein ion channels and energy storage devices. Direct microscopic observations are critical for understanding the intricacy of ionic processes in nanoconfinement. Here we report an in situ nuclear magnetic resonance study of ion hydration in voltage-gated carbon nanopores.

Electroneutrality breakdown and specific ion effects in nanoconfined aqueous electrolytes observed by NMR.

Ion distribution in aqueous electrolytes near the interface plays a critical role in electrochemical, biological and colloidal systems, and is expected to be particularly significant inside nanoconfined regions. Electroneutrality of the total charge inside nanoconfined regions is commonly assumed a priori in solving ion distribution of aqueous electrolytes nanoconfined by uncharged hydrophobic surfaces with no direct experimental validation. Here, we use a quantitative nuclear magnetic resonance approach to investigate the properties of aqueous electrolytes nanoconfined in graphitic-like nanoporous carbon.