ABSTRACT
This review presents an in-depth analysis of the immense potential of CRISPR-Cas9 technology in revolutionizing oral cancer research. It underscores the inherent limitations of conventional treatments while emphasizing the pressing need for groundbreaking approaches. The unparalleled capability of CRISPR-Cas9 to precisely target and modify specific genes involved in cancer progression heralds a new era in therapeutic intervention. Employing genome-wide CRISPR screens, vulnerabilities in oral cancer cells can be identified, thereby unravelling promising targets for therapeutic interventions. In the realm of oral cancer, the disruptive power of CRISPR-Cas9 manifests through its capacity to perturb genes that are intricately associated with drug resistance, consequently augmenting the efficacy of chemotherapy. To address the challenges that arise, this review diligently examines pertinent issues such as off-target effects, efficient delivery mechanisms, and the ethical considerations surrounding germline editing. Through precise gene editing, facilitated by CRISPR/Cas9, it becomes possible to overcome drug resistance by rectifying mutations, thereby enhancing the efficacy of personalized treatment strategies. This review delves into the prospects of CRISPR-Cas9, illuminating its potential applications in the domains of medicine, agriculture, and biotechnology. It is paramount to emphasize the necessity of ongoing research endeavors and the imperative to develop targeted therapies tailored specifically for oral cancer. By embracing this comprehensive overview, we can pave the way for ground-breaking treatments that instill renewed hope for enhanced outcomes in individuals afflicted by oral cancer.
ABSTRACT
Background: Diabetic foot ulcer (DFU) is a serious infectious disease, which can be managed very well through proper medication, but if left untreated it may lead to amputation of the affected area leading to permanent immobility which will compromise the patient's quality of life. Objective: To carry out prescription auditing and to assess medication adherence patterns among DFU patients. Materials and methods: A prospective interview-based study was carried out in the surgery department among inpatients aged above 18 years diagnosed with a DFU. A disease specific medication adherence questionnaire consisting of 15 questions was developed, validated and implemented among 65 patients. All the relevant data were analyzed and subjected to statistical analysis. Results: The majority (49.2%) of the population had an intermediate adherence pattern followed by high adherence (43%) during the first visit, later the reassessment after the counseling, reports a gradual increase in adherence pattern. Prescription auditing reveals the intensity of drug burden, on an average a prescription might carry 8 drugs and more than 95.3% of prescribed drugs were in injectable form. Conclusion: This study highlights the depth of drug burden through prescription auditing and the necessity of proper patient counseling and educating the patient about the importance of medication adherence for limiting disease progression.
ABSTRACT
Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.
ABSTRACT
The clinical management of oral cancer is often frequented with challenges that arise from relapse, recurrence, invasion and resistance towards the cornerstone chemo and radiation therapies. The recent conceptual advancement in oncology has substantiated the role of cancer stem cells (CSC) as a predominant player of these intricacies. CSC are a sub-group of tumor population with inherent adroitness to self-renew with high plasticity. During tumor evolution, the structural and functional reprogramming persuades the cancer cells to acquire stem-cell like properties, thus presenting them with higher survival abilities and treatment resistance. An appraisal on key features that govern the stemness is of prime importance to confront the current challenges encountered in oral cancer. The nurturing niche of CSC for maintaining its stemness characteristics is thought to be modulated by complex multi-layered components encompassing neoplastic cells, extracellular matrix, acellular components, circulatory vessels, various cascading signaling molecules and stromal cells. This review focuses on recapitulating both intrinsic and extrinsic mechanisms that impart the stemness. There are contemplating evidences that demonstrate the role of transcription factors (TF) in sustaining the neoplastic stem cell's pluripotency and plasticity alongside the miRNA in regulation of crucial genes involved in the transformation of normal oral mucosa to malignancy. This review illustrates the interplay between miRNA and various known TF of oral cancer such as c-Myc, SOX, STAT, NANOG and OCT in orchestrating the stemness and resistance features. Further, the cross-talks involved in tumor micro-environment inclusive of cytokines, macrophages, extra cellular matrix, angiogenesis leading pathways and influential factors of hypoxia on tumorigenesis and CSC survival have been elucidated. Finally, external factorial influence of oral microbiome gained due to the dysbiosis is also emphasized. There are growing confirmations of the possible roles of microbiomes in the progression of oral cancer. Given this, an attempt has been made to explore the potential links including EMT and signaling pathways towards resistance and stemness. This review provides a spectrum of understanding on stemness and progression of oral cancers at various regulatory levels along with their current therapeutic knowledge. These mechanisms could be exploited for future research to expand potential treatment strategies.
ABSTRACT
Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.
ABSTRACT
Cancer is a leading cause of death worldwide. Despite many research advancements in the field, the genetic changes regulating the transformation of normal oral cells into malignant cells have not been fully elucidated. Several studies have evaluated carcinogenesis at the molecular level. Cancer cell lines are commonly used in biomedical research because they provide an unlimited source of cells and represent various stages of initiation and progression of carcinogenesis in vitro. Aims: The objective of the study was to review original research articles using cancer cell lines as a tool to understand carcinogenesis and to identify the genes involved in tumor development. Additionally, we also examined the application of the genes as predictive biomarkers. Methods and Materials: Several databases, including PubMed, Google Scholar, Ebsco, and Science Direct, were searched from 1985 to December 2016 using various combinations of the following key words: "mouth neoplasm", "cell lines", and "tumorigenesis". Original experimental studies published in English were included. We excluded letters to the editor, historic reviews, and unpublished data from the analysis. Results: There were 17 studies (in vitro) included in the analysis. There were 14 genes and 4 miRNAs involved in malignant transformation of oral keratinocytes into cancer cells. The most commonly studied genes were p53, cyclin D1, and hTERT. Conclusion: Additional reviews and studies are needed to identify a panel of genes specific to various potentially malignant disorders and to aid in the early detection of oral squamous cell carcinoma (OSCC) because tumorigenesis involves the mutation of multiple genes. Furthermore, improving advanced cost-effective diagnostic methods may benefit the public health sector.
