Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.

Machine Learning-Based Gesture Recognition Glove: Design and Implementation.

In the evolving field of human-computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor.

Sacituzumab Govitecan for Second and Subsequent Line Palliative Treatment of Patients with Triple-Negative Breast Cancer: A Polish Real-World Multicenter Cohort Study.

Introduction: Sacituzumab govitecan (SG) is approved for patients with previously treated metastatic or locally advanced triple-negative breast cancer (TNBC), as per the ASCENT trial results. Real-world studies (RWSs) cover more diverse patients than clinical trials, offering crucial data for healthcare policies. This study aimed to investigate the safety and efficacy of SG in real-world Polish patients with previously treated metastatic TNBC.

Treatment outcomes and prognostic factors in nonmetastatic metaplastic breast cancer patients: a multicenter retrospective cohort study.

Background And Purpose: Metaplastic breast carcinoma (BC-Mp) is an uncommon subtype that poses unique challenges. The limited information on patient prognosis and therapeutic strategies motivated our research initiative. We aimed to assess disease-free survival (DFS), overall survival (OS), and influential factors in patients with nonmetastatic BC-Mp.