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. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.

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 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Synthesis, Structural Studies and Biological Evaluation of Connections of Thiosemicarbazide, 1,2,4-Triazole and 1,3,4-Thiadiazole with Palmitic Acid.

Thirty new derivatives of palmitic acid were efficiently synthesized. All obtained compounds can be divided into three groups of derivatives: Thiosemicarbazides (compounds 1-10), 1,2,4-triazoles (compounds 1a-10a) and 1,3,4-thiadiazoles (compounds 1b-10b) moieties. ¹H-NMR, 13C-NMR and MS methods were used to confirm the structure of derivatives. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds 4, 5, 6, 8 showed significant inhibition against C. albicans. The range of MIC values was 50-1.56 µg/mL. The halogen atom, especially at the 3rd position of the phenyl group was significantly important for antifungal activity. The biological activity against Candida albicans and selected molecular descriptors were used as a basis for QSAR models, that have been determined by means of multiple linear regression. The models have been validated by means of the Leave-One-Out Cross Validation. The obtained QSAR models were characterized by high determination coefficients and good prediction power.

Statistical Analysis of the Impact of Molecular Descriptors on Cytotoxicity of Thiourea Derivatives Incorporating 2-Aminothiazole Scaffold.

Chemical reactivity descriptors and lipophilicyty (log P) were evaluated via semi-empirical method for the quantum calculation of molecular electronic structure (PM3) in order to clarify the structure-cytotoxic activity relationships of disubstutited thioureas. Analysed compounds were obtained by the linkage of 2-aminothiazole ring, thiourea and substituted phenyl ring. The detailed examination was carried out to establish correlation between descriptors and cytotoxic activity against the MT-4 cells for 11 compounds. For the most active compounds (6 compounds) cytotoxic activity against three cancer cell lines (CCRF-CEM, WIL-2NS, CCRF-SB) and normal human cell (HaCaT) was determined. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reduction and lactate dehydrogenase (LDH) release were assessed. Regression analysis revealed that electrophilicity index and chemical potential significantly contributed to expain the thioureas cytotoxic potential.

Antimicrobial and anti-biofilm activity of thiourea derivatives incorporating a 2-aminothiazole scaffold.

A series of new thiourea derivatives of 1,3-thiazole have been synthesized. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds were also tested for their in vitro tuberculostatic activity against the Mycobacterium tuberculosis H37Rv strain, as well as two 'wild' strains isolated from tuberculosis patients. Compounds 3 and 9 showed significant inhibition against Gram-positive cocci (standard strains and hospital strain). The range of MIC values is 2-32 µg/mL. Products 3 and 9 effectively inhibited the biofilm formation of both methicillin-resistant and standard strains of S. epidermidis. The halogen atom, especially at the 3rd position of the phenyl group, is significantly important for this antimicrobial activity. Moreover, all obtained compounds resulted in cytotoxicity and antiviral activity on a large set of DNA and RNA viruses, including Human Immunodeficiency Virus type 1 (HIV-1) and other several important human pathogens. Compound 4 showed activity against HIV-1 and Coxsackievirus type B5. Seven compounds resulted in cytotoxicity against MT-4 cells (CC50<10 µM).

Anticancer activities of fatty acids and their heterocyclic derivatives.

Traditional chemotherapy relies on the premise that rapidly proliferating cancer cells are more likely to be killed by a cytotoxic agent, but in reality, the long-standing problem of chemotherapy is the lack of tumor-specific treatments. Apart from the impact on tumor cells, the drugs' major limitation is their severe adverse side effects on normal cells and tissues. Nutritional and epidemiological studies have indicated that cancer progression is correlated with the consumption of fatty acids, but the exact mechanisms still remain unknown. In the first part of our review, we discussed the beneficial effects of free fatty acids (saturated and unsaturated) on the progress of carcinogenesis in different tumor cell lines. We presented various mechanisms proposed in the literature, which explain the possible impact on the cells metabolism. The second part describes modifications of different fatty acids with existing anticancer drugs and heterocyclic moieties by condensation reactions. Such conjugations increased the tissue selectivity and made chemotherapy potentially more effective and less toxic in in vivo and in vitro studies. This fatty acid modifications, which change the activity of compounds, their uptake selectivity and alter drug delivery methods, may be the key to unlocking true medical potential of fatty acids.

GABAB receptor intracellular signaling: novel pathways for depressive disorder treatment?

Affecting over 320 million people around the world, depression has become a formidable challenge for modern medicine. In addition, an increasing number of studies cast doubt on the monoamine theory of depressive disorder and, worryingly, antidepressant medications only significantly benefit patients with severe depression. Thus, it is not surprising that researchers have shown an increased interest in new theories attempting to explain the pathogenesis of this disease. One example is the excitatory/inhibitory transmission imbalance theory. These abnormalities involve glutamate and γ-aminobutyric acid (GABA) signaling. Studies on GABAB receptors and their antagonists are particularly promising for the treatment of depressive disorders. In this paper, intracellular pathways controlled by GABAB receptors and their links to depression are described, including the impact of ketamine on GABAergic synaptic transmission.

Assessment of cancer stem cell marker expression in primary head and neck squamous cell carcinoma shows prognostic value for aldehyde dehydrogenase (ALDH1A1).

Cancer stem cells (CSCs) play a key role in carcinogenesis and progression of head and neck squamous cell carcinomas (HNSCC). The most common markers indicating for CSCs are: CD44, CD24, CD133, ALDH1A1. Our objective was to evaluate the prognostic potential of CSC markers in HNSCC. The study included 49 patients treated for primary HNSCC, 11 patients with upper respiratory tract epithelial dysplasia and 12 subjects with the normal pharyngeal mucosa as a control group. The frequency and expression levels of the four CSC markers were assessed by immunohistochemistry. Univariate and multivariate analyses were used to correlate CSC expression levels with tumor stage, lymph node metastases or overall survival (OS). CD44, CD24, CD133, ALDH1A1 were widely expressed in tumors, whereas CD44 was found to be higher in cancer tissue (P = 0.001). ALDH1A1 expression levels were found to be significantly higher in T3-T4 tumors vs. T1-T2 tumors (P = 0.05). Lymph node metastases had significantly higher expression levels of CD24 (P = 0.01) and CD133 (P < 0.05) than primary tumors. Multifactorial analysis revealed that overall survival (OS) for patients with ALDH1A1 negative tumors was 5.25 times higher than for patients with ALDH1A1 positive (ALDH1A1+) tumors (P = 0.01). On univariate and multivariate analysis, only ALDH1A1 positivity had a significant effect on OS of HNSCC patients (HR = 2.47 for P = 0.02). Immunohistochemistry-based assessments of CSC marker expression in HNSCC has significant predictive implications for patients with HNSCC. The frequency of CSCs in the tumor, specifically of ALDH1A1+ cells correlated with five-year OS in these patients.