Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART).

A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1.

Wireless system for recording evoked potentials.

Experiments measuring evoked potentials require flexible and rapid adjustment of stimulation and recording parameters. In this study, we have developed a recording system and an associated Android application that allow making such adjustments wirelessly. The system consists of 3 units: for stimulation, recording and control.

Optimal Delivery of Pain Management in Schwannomatosis: A Literature Review.

Non-NF2 schwannomatosis is a rare syndrome characterized by multiple benign schwannomas that primarily affect nerve sheaths, with chronic, treatment-resistant pain as the most common symptom. No protocol has been established for pain management, and pharmacotherapies, including molecular target therapies, are being evaluated. Neuromodulation therapies such as scrambler therapy and surgical options are also employed; however, surgery may lead to persistent or recurrent pain caused by nerve damage or tumor recurrence.

Association of the Glasgow Coma Scale with the Length of Hospital Stay in Patients with Thyroid Storm.

Objective The Glasgow Coma Scale (GCS) is widely used to assess the levels of consciousness. This study examined whether or not the initial GCS score could be a marker of the length of hospital stay (LOS) in patients with thyroid storm. Methods We retrospectively analyzed 29 patients with thyroid storm in our hospital between January 2010 and December 2023.

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment.

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties.