The effect of low-level laser therapy on the oxidative stress level and quality of life in patients with Hashimoto's thyroiditis.

This study aimed to examine the effects of low-level laser therapy (LLLT) combined with levothyroxine replacement therapy on thyroid function, oxidative stress (OS), and quality of life in patients with Hashimoto's thyroiditis (HT). Forty-six patients diagnosed with HT were randomized to receive active LLLT (n = 23) and sham LLLT (n = 23) twice a week for three weeks. Clinical and laboratory evaluations of the participants were performed before treatment and three months after treatment. Biochemical parameters were taken from the patient file requested by the physician as a routine examination. Malondialdehyde and nitricoxide indicating oxidant stress and superoxide dismutase, catalase, and glutathione, which indicate antioxidant capacity, were used in OS evaluation. The Oxidative Stress Index was calculated by measuring the Total Antioxidant Status and the Total Oxidant Status. At the end of our study, a significant improvement in oxidant and antioxidant biomarker levels showing OS and quality of life was observed in the treatment groups (p < 0.05). There was no change in thyroid function and autoimmunity at the end of the treatment between the two groups (p > 0.05). Improvements in glutathione levels and quality of life were significantly higher in the active treatment group than in the sham-controlled group. LLLT was found to be more effective on OS and quality of life in patients with HT than in patients in the sham-controlled group. It was concluded that LLLT is a safe and effective method that can be used in the treatment of patients with HT.

Modeling tumor growth using fractal calculus: Insights into tumor dynamics.

Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sigmoidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models)". We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates "double-size cancer" in the fractal temporal dimension with respect to various mathematical models.

Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics.

To explore the crossover linkage of the bacterial infections resulting from the viral infection, within the host body, a computational framework is developed. It analyzes the additional pathogenic effect of Streptococcus pneumonia, one of the bacteria that can trigger the super-infection mechanism in the COVID-19 syndrome and the physiological effects of innate immunity for the control or eradication of this bacterial infection. The computational framework, in a novel manner, takes into account the action of pro-inflammatory and anti-inflammatory cytokines in response to the function of macrophages. A hypothetical model is created and is transformed to a system of non-dimensional mathematical equations. The dynamics of three main parameters (macrophages sensitivity κ , sensitivity to cytokines η and bacterial sensitivity ϵ ), analyzes a "threshold value" termed as the basic reproduction number R 0 which is based on a sub-model of the inflammatory state. Piece-wise differentiation approach is used and dynamical analysis for the inflammatory response of macrophages is studied in detail. The results shows that the inflamatory response, with high probability in bacterial super-infection, is concomitant with the COVID-19 infection. The mechanism of action of the anti-inflammatory cytokines is discussed during this research and it is observed that these cytokines do not prevent inflammation chronic, but only reduce its level while increasing the activation threshold of macrophages. The results of the model quantifies the probable deficit of the biological mechanisms linked with the anti-inflammatory cytokines. The numerical results shows that for such mechanisms, a minimal action of the pathogens is strongly amplified, resulting in the "chronicity" of the inflammatory process.

Forecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process.

The CAR-T cells are the genetically engineered T cells, designed to work specifically for the virus antigens (or other antigens, such as tumour specific antigens). The CAR-T cells work as the living drug and thus provides an adoptive immunotherapy strategy. The novel corona virus treatment and control designs are still under clinical trials. One of such techniques is the injection of CAR-T cells to fight against the COVID-19 infection. In this manuscript, the hypothesis is based on the CAR-T cells, that are suitably engineered towards SARS-2 viral antigen, by the N protein. The N protein binds to the SARS-2 viral RNA and is found in abundance in this virus, thus for the engineered cell research, this protein sequence is chosen as a potential target. The use of the sub-population of T-reg cells is also outlined. Mathematical modeling of such complex line of action can help to understand the dynamics. The modeling approach is inspired from the probabilistic rules, including the branching process, the Moran process and kinetic models. The Moran processes are well recognized in the fields of artificial intelligence and data science. The model depicts the infectious axis "virus-CAR-T cells-memory cells". The theoretical analysis provides a positive therapeutic action; the delay in viral production may have a significant impact on the early stages of infection. Although it is necessary to carefully evaluate the possible side effects of therapy. This work introduces the possibility of hypothesizing an antiviral use by CAR-T cells.

Furin and the adaptive mutation of SARS-COV2: a computational framework.

SARS-2 virus has reached its most harmful mutated form and has damaged the world's economy, integrity, health system and peace to a limit. An open problem is to address the release of antibodies after the infection and after getting the individuals vaccinated against the virus. The viral fusion process is linked with the furin enzyme and the adaptation is linked with the mutation, called D614G mutation. The cell-protein studies are extremely challenging. We have developed a mathematical model to address the process at the cell-protein level and the delay is linked with this biological process. Genetic algorithm is used to approximate the parametric values. The mathematical model proposed during this research consists of virus concentration, the infected cells count at different stages and the effect of interferon. To improve the understanding of this model of SARS-CoV2 infection process, the action of interferon (IFN) is quantified using a variable for the non-linear mathematical model, that is based on a degradation parameter γ . This parameter is responsible for the delay in the dynamics of this viral action. We emphasize that this delay responds to the evasion by SARS-CoV2 via antagonizing IFN production, inhibiting IFN signaling and improving viral IFN resistance. We have provided videos to explain the modeling scheme.

Analysis of Trabecular Bone Mechanics Using Machine Learning.

"Bone remodeling" is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of "drug design, treatment, and therapy" of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data.

Responses of grapevine rootstocks to drought through altered root system architecture and root transcriptomic regulations.

Roots are the major interface between the plant and various stress factors in the soil environment. Alteration of root system architecture (RSA) (root length, spread, number and length of lateral roots) in response to environmental changes is known to be an important strategy for plant adaptation and productivity. In light of ongoing climate changes and global warming predictions, the breeding of drought-tolerant grapevine cultivars is becoming a crucial factor for developing a sustainable viticulture. Root-trait modeling of grapevine rootstock for drought stress scenarios, together with high-throughput phenotyping and genotyping techniques, may provide a valuable background for breeding studies in viticulture. Here, tree grafted grapevine rootstocks (110R, 5BB and 41B) having differential RSA regulations and drought tolerance were investigated to define their drought dependent root characteristics. Root area, root length, ramification and number of root tips reduced less in 110R grafted grapevines compared to 5BB and 41B grafted ones during drought treatment. Root relative water content as well as total carbohydrate and nitrogen content were found to be much higher in the roots of 110R than it was in the roots of other rootstocks under drought. Microarray-based root transcriptome profiling was also conducted on the roots of these rootstocks to identify their gene regulation network behind drought-dependent RSA alterations. Transcriptome analysis revealed totally 2795, 1196 and 1612 differentially expressed transcripts at the severe drought for the roots of 110R, 5BB and 41B, respectively. According to this transcriptomic data, effective root elongation and enlargement performance of 110R were suggested to depend on three transcriptomic regulations. First one is the drought-dependent induction in sugar and protein transporters genes (SWEET and NRT1/PTR) in the roots of 110R to facilitate carbohydrate and nitrogen accumulation. In the roots of the same rootstock, expression increase in osmolyte producer genes revealed another transcriptomic regulation enabling effective root osmotic adjustment under drought stress. The third mechanism was linked to root suberization with upregulation of transcripts functional in wax producing enzymes (Caffeic acid 3-O-methyltransferase, Eceriferum3, 3-ketoacyl-CoAsynthase). These three transcriptomic regulations were suggested to provide essential energy and water preservation to the roots of 110R for its effective RSA regulation under drought. This phenotypic and genotypic knowledge could be used to develop root-dependent drought tolerant grapevines in breeding programs and could facilitate elucidation of genetic regulations behind RSA alteration in other plants.