Effects of Transcranial and Trans-Spinal Direct Current Stimulation Combined with Robot-Assisted Gait Training on Gait and Fatigue in Patients with Multiple Sclerosis: A Double-Blind, Randomized, Sham-Controlled Study.

Multiple Sclerosis (MS) is a chronic neurological condition that impairs motor and sensory functions, particularly gait. Non-invasive neuromodulation techniques aim to enhance functional recovery and motor-cognitive outcomes, though their effectiveness remains debated. This study compared the effects of transcranial direct current stimulation (tDCS) and trans-spinal direct current stimulation (tsDCS), combined with robotic-assisted gait training (RAGT), on motor function and fatigue in people with MS (pwMS).

Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A Double-Blind, Randomized, Crossover Study.

Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson's disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD.

Selective nociceptive modulation using a novel prototype of transcutaneous kilohertz high-frequency alternating current stimulation: a crossover double-blind randomized sham-controlled trial.

Background: Kilohertz high-frequency alternating current (KHFAC) stimulation has demonstrated to induce rapid and reversible nerve blocks without causing nerve damage. Previous studies have explored frequency-dependent effects using a transcutaneous approach in humans from 5 to 20 kHz. Nevertheless, its application in humans is limited by the lack of stimulators approved for frequencies above 20 kHz.

Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting.