Evaluation of Antibacterial Activity of Pine Tar on Periodontal Pathogenic Bacteria: An In Vitro Study

BACKGROUND: Periodontal pathogens play an important role in etiology and pathogenesis of periodontitis. Microbiological examination of sub gingival plaque is used at the present time in etiological research as well as in clinical treatment of periodontitis to select the appropriate antibiotic agent if indicated. Pine tar has been used for the treatment of various skin diseases. So the study was done to evaluate the effect of Pine Tar oil on bacteria isolated from periodontitis patients.METHODS: Plaque samples from volunteer patients were collected using sterile paper points. Robertson's Cooked Meat (RCM) medium was used for the transportation and cultivation of aerobic, microaerophilic, and anaerobic microorganisms.RESULTS: The result suggests the use of Pine tar oil for topical application in periodontal diseases. Disc diffusion analysis was sufficient enough to illustrate that 75 μl tar oil solution produced growth inhibition of microbial strains.CONCLUSION: Pine tar oil has become one of the important areas of research both in pharmaceutical and periodontal research, hence in vivo studies has to be carried out with various form of pine tar.&nbsp

Assessment of <i>Helicobacter pylori </i>cytotoxin-associated Gene A (Cag A) protein and its association with ferritin and vitamin B12 deficiencies among adult healthy asymptomatic residents in Sharjah, United Arab Emirates

Introduction: The United Arab Emirates (UAE) serves as an effective epidemiological site for assessing Helicobacter pylori (H. pylori) infection due to its diverse population. However, comprehensive studies on the prevalence of H. pylori in the UAE are notably scarce. In depth prevalence studies are needed as a preventive measure against gastric cancer and other emerging extra gastric diseases associated with H. pylori infection. Aim: This study aimed to assess H. pylori infection and its virulent oncoprotein, the Cytotoxin-Associated Gene (Cag A) and its association with ferritin and vitamin B12 deficiencies. Methods: The study was conducted on 1094 healthy asymptomatic volunteers residents in the Sharjah Emirate, UAE. Enzyme-linked immunosorbent assay (ELISA) was performed to assess H. pylori infection using H. pylori antibodies (IgG), and detection of CagA protein using Cag A antibody (IgG) in the human serum. Ferritin and vitamin B12 serum levels were assessed and correlated to H. pylori infection. Results: This study focuses mainly on the assessment of H. pylori and its virulent factor CagA, in relation to vitamin B12 and ferritin deficiencies. Remarkably, 49.6 % of the participants were detected positive for H. pylori, with over half of these cases involving CagA positive strains. Notably, among Emirati participants, 76.11 % of those with H. pylori infection were CagA positive. Statistical analysis revealed a significant correlation between H. pylori, CagA level, and ferritin/vitamin B12 deficiencies. Conclusion: These findings emphasize the importance of timely detection and eradication of H. pylori not only as a preventive strategy against gastric cancer but also as an effective strategy to rescue the adverse effects from ferritin and vitamin B12 deficiencies, thereby improving the overall health outcomes of individuals affected by H. pylori infection.</p

Emerging role of caldesmon in cancer: A potential biomarker for colorectal cancer and other cancers

Colorectal cancer (CRC) is a devastating disease, mainly because of metastasis. As a result, there is a need to better understand the molecular basis of invasion and metastasis and to identify new biomarkers and therapeutic targets to aid in managing these tumors. The actin cytoskeleton and actin-binding proteins are known to play an important role in the process of cancer metastasis because they control and execute essential steps in cell motility and contractility as well as cell division. Caldesmon (CaD) is an actin-binding protein encoded by the CALD1 gene as multiple transcripts that mainly encode two protein isoforms: High-molecular-weight CaD, expressed in smooth muscle, and low-molecular weight CaD (l-CaD), expressed in nonsmooth muscle cells. According to our comprehensive review of the literature, CaD, particularly l-CaD, plays a key role in the development, metastasis, and resistance to chemoradiotherapy in colorectal, breast, and urinary bladder cancers and gliomas, among other malignancies. CaD is involved in many aspects of the carcinogenic hallmarks, including epithelial mesenchymal transition via transforming growth factor-beta signaling, angiogenesis, resistance to hormonal therapy, and immune evasion. Recent data show that CaD is expressed in tumor cells as well as in stromal cells, such as cancerassociated fibroblasts, where it modulates the tumor microenvironment to favor the tumor. Interestingly, CaD undergoes selective tumor-specific splicing, and the resulting isoforms are generally not expressed in normal tissues, making these transcripts ideal targets for drug design. In this review, we will analyze these features of CaD with a focus on CRC and show how the currently available data qualify CaD as a potential candidate for targeted therapy in addition to its role in the diagnosis and prognosis of cancer

Data-driven-based vector space decomposition modeling of multiphase induction machines

For contemporary variable-speed electric drives, the accuracy of the machine's mathematical model is critical for optimal control performance. Basically, phase variables of multiphase machines are preferably decomposed into multiple orthogonal subspaces based on vector space decomposition (VSD). In the available literature, identifying the correlation between states governed by the dynamic equations and the parameter estimate of different subspaces of multiphase IM remains scarce, especially under unbalanced conditions, where the effect of secondary subspaces sounds influential. Most available literature has relied on simple RL circuit representation to model these secondary subspaces. To this end, this paper presents an effective data-driven-based space harmonic model for n-phase IMs using sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover the IM governing equations. Moreover, the proposed approach is computationally efficient, and it precisely identifies both the electrical and mechanical dynamics of all subspaces of an IM using a single transient startup run. Additionally, the derived model can be reformulated into the standard canonical form of the induction machine model to easily extract the parameters of all subspaces based on online measurements. Eventually, the proposed modeling approach is experimentally validated using a 1.5 Hp asymmetrical six-phase induction machine