Current Signal Transduction Therapy - Volume 19, Issue 1, 2024

Background: Transcranial photobiomodulation (PBM) therapy has emerged as a promising alternative therapeutic option for the management of neurological and psychiatric disorders. However, the underlying mechanisms of PBM therapy and its effects on motor performance in brain disorders are not yet fully understood. The aim of this literature review is to provide a more detailed and evidence-based explanation of the rationale and intent behind the correlation between PBM therapy and its effects on motor performance in brain disorders. Methods: A literature search was performed in the databases "PubMed/Medline", "Scopus," and "Google Scholar" for all relevant English language papers. A combination of different keywords was used for the database search. Video articles, patents, review articles, book chapters, articles using other transcranial methods, non-transcranial PBM, and case reports were excluded. Results: Out of the 2174 papers, 18 addressed the effect of PBM on motor performance. Among these, four studies were on ischemic stroke models and individuals with stroke, six studies on models associated with traumatic brain injury (TBI), five studies on models associated with neurodegenerative diseases and Parkinson's disease, and four studies related to models and patients with central nervous system inflammation. All studies have shown that motor parameters improve with PBM. In two studies on healthy individuals, 65 showed improvement in motor function and 16 showed improvement in motor evoked potential. In most studies (n=10), the wavelength used was between 800 and 900 nm. Near-infrared or LED continuous light was used in most studies. However, two studies compared the effects of pulsed and continuous waves and found the superiority of pulsed over continuous waves. Conclusions: PBM therapy appears to be useful in brain injury, inducing changes at the behavioral, motor, cellular, and chemical levels. Recent studies suggest that PBM therapy may have potential benefits in improving motor performance in brain disorders, including stroke, traumatic brain injury, Parkinson's disease, and demyelination. However, further research is needed to determine the optimal parameters for PBM therapy and to investigate its effects on motor function in different brain disorders. Overall, PBM therapy appears to be a promising therapeutic option for brain injury and warrants further investigation.

Background: Hepatocellular carcinoma (HCC) is the most common type of cancer. Although HCC treatment has greatly improved over the past few decades, patient survival rates are still very low. Therefore, it is essential to find new treatments for HCC. Apoptosis has been shown to be the most effective in disrupting cancer growth. Improper functioning of proteins in apoptosis can lead to cancer growth. MicroRNAs (miRNAs) are key regulators in the development and progression of HCC. Objective: Irregular expression of miRNAs involved in apoptosis signaling can lead to tumorigenesis. Therefore, we investigated the effect of the hsa-miR-4270 inhibitor on cell proliferation and apoptosis. Methods: HepG2 cells were cultured at 37°C and 95% air. Transfection of HepG2 cells was performed by miR-4270 inhibitor and lipofectamine 2000. Cell proliferation of HepG2 cells was determined with MTT assay and different concentrations of miR-4270 specific inhibitors. DNA laddering assay was performed to evaluate the induction of apoptosis. Finally, the transcription level of genes involved in apoptosis, including BAX, BCL2, Caspase3, and p53, was measured by real-time RT-PCR. Results: The results of MTT and DNA laddering assays showed that the miR-4270 inhibitor declined cell proliferation and induced apoptosis in HepG2 cells. Also, the results of quantitative real-time RT-PCR indicated an upregulation of transcription of BAX, p53 and Caspase3 genes and a decline in expression of BCL2 gene. Conclusion: Taken together, we found hsa-miR-4270 inhibitor decreased cell proliferation and induced apoptosis in the HepG2 cell line, which can be used as a new therapeutic strategy for HCC patients.

Breast cancer is one of the most common malignancies in women worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the most common kinds of invasive breast cancer. Several genetic, epigenetic, and environmental factors could trigger the pathogenesis of breast cancer. Breast cancer treatment generally includes surgery, radiation therapy, chemotherapy, hormonal treatment, targeted therapy, immunotherapeutic, neoadjuvant systemic therapy, and systemic therapy. Although several classical treatment methods are used in cancer therapy, molecular-based strategies can open a new perspective for breast cancer treatment. Previous studies reported that long non-coding RNAs (lncRNAs) play important roles in cancer development and progression. LncRNA TUG1 was found to target several miRNAs and regulate breast cancer cell behavior. TUG1 can induce cell proliferation and invasion of breast cancer cells via downregulation of some miRNAs. Therefore, TUG1 might be a potent biomarker for the treatment of human cancer. In this review, we summarized the functional roles of TUG1 in breast cancer.

Background: Leishmaniasis is a phagocytic host cell invading, caused by leishmania species mostly found in developing nations. To treat leishmaniasis, a wide range of medications and potential vaccines are available, such as pentavalent antimonials, amphotericin, and miltefosined, but due to the lack of effective treatments, the high toxicity of chemotherapy and the growth of drug resistance linked to these diseases necessitate the urgent development of innovative therapeutic agents. β-carboline is a group of chemical compounds, and its derivatives have shown to be one of the potential candidates for the treatment of Leishmania. The carboline derivatives may have antileishmanial activity. Which act by interfering with the parasite’s DNA replication or metabolic processes, inhibiting the enzymes that are mainly responsible for the reproduction and metabolism of leishmania. Objective: This research undertaking's present focus is on determining the existence and virtual screening of potential β-carboline derivative, which can act as an antileishmanial agent that can prevent or stop the progression of leishmania by the integration of different computational technologies such as in silico ADMET analysis and docking. Methods: After synthesizing molecules using virtual screening, all designed compounds underwent molecular docking, and hit molecules underwent ADMET analysis. Results: Using AutoDock Vina 1.5.6, molecular docking was carried out in the arginase receptor's active site (PDBID: 2AEB). The research and creation of compounds used virtual screening. Fifteen hits from docking experiments had high binding affinity as, in comparison to the commercially available molecule pentamidine (binding score -5.5 Kcal/mol), 15 compounds (binding affinity -7.9 to 7.0 Kcal/mol). These Fifteen best hits were further examined for their ADME activity using SwissADME, and the ADME analysis identified 15 medicines as having the ideal ADME profile and improved bioavailability. Conclusion: This research will bring up a broad spectrum of prospects for investigation into the disciplines of computational and medical research. This will facilitate the development of new antileishmanial agents that have better stability, bioavailability, and less toxicity, side effects for use in future research studies.

Introduction: Electromyography (EMG) signals are contaminated by various noise components. These noises directly degrade the EMG processing performance, thereby affecting the classification accuracy of the EMG signals for implementing various hand movements of the prosthetic arm from the amputee's residual muscle. Methods: This study mainly aims to denoise the EMG signals using the empirical mode decomposition (EMD) and correlation coefficient thresholding (CCT) methods. The noisy EMG signal is obtained from NinaPro Database 2. Then, EMD is used to decompose it into intrinsic mode functions. Each hand movement noise is identified within specific modes and removed separately using correlation coefficient–dependent thresholding and wavelet denoising. The performance metrics signal-to-noise ratio (SNR) and root mean square error (RMSE) were used to evaluate the noise removal performance from the EMG signals of five intact subjects. The proposed method outperforms the wavelet denoising method in terms of noise interference removal. In this method, the SNR is obtained in the 17-22 dB range with a very low RMSE. Results: The experimental results illustrate that the proposed method removes noise from six repetitions of six movements performed by five subjects. This study explores the special characteristics of EMD and demonstrates the possibility of using the EMD-based CCT filter for denoising EMG signals. Conclusion: The proposed filter is more efficient than wavelet denoising in removing noise interference. It can also be used in any application that requires EMG signal filtering at the preprocessing stage, such as EMG pattern recognition.

Background: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

Wound healing is a highly intricate process that involves a coordinated interplay of various cellular and molecular events. In recent years, mesenchymal stem cells (MSCs) have garnered significant attention as a promising therapeutic modality due to their remarkable regenerative properties. In addition to their capacity for direct tissue repair and differentiation, MSCs release a diverse repertoire of bioactive factors into the surrounding microenvironment, collectively termed an MSC-conditioned Medium (MSC-CM). This review aims to elucidate the therapeutic potential of MSC-CM as a multifactorial approach to enhance wound healing. MSCCM encompasses an extensive range of factors, including growth factors, cytokines, chemokines, extracellular matrix proteins, and non-coding RNAs, which collectively orchestrate and stimulate multiple cellular processes crucial for effective wound healing. Notably, these factors play pivotal roles in promoting angiogenesis, stimulating the proliferation and migration of various cell types involved in wound repair, modulating the immune response, and facilitating extracellular matrix remodeling. Moreover, compelling evidence from preclinical and clinical studies demonstrates that MSC-CM administration accelerates wound closure, enhances reepithelialization, and improves the quality of granulation tissue. By harnessing the therapeutic potential of MSC-CM, we can pave the way for significant advancements in wound care, ultimately leading to improved patient outcomes.