Yazar "Sohail, Ayesha" seçeneğine göre listele

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    Analysis of trabecular bone mechanics using machine learning
    (Sage Publications Ltd, 2019) Sohail, Ayesha; Younas, Muhammad; Bhatti, Yousaf; Li, Zhiwu; Tunç, Sümeyye; Abid, Muhammad
    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.
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    Computational framework to explore impact of environmental stress on epidemics
    (World Scientific, 2020) Sohail, Ayesha; Idrees, Muhammad; Sajjad, Maria; Iftikhar, Sahrish; Tunç, Sümeyye
    In the field of epidemiology, not only the disease and the carriers, but also the surrounding environment and the associated stresses play a vital role. Environmental stresses in a novel habitat may facilitate adaptive shifts. Organisms living under environmental stresses often experience higher mutation rates and display greater phenotypic and genetic variation. There is controversial evidence available in the literature about the impact of environmental stresses on the organisms and the resulting variation in mutation rates and the immune responses. In nature, "selection"and the high energetic costs of stress usually reduce this variation. The prior knowledge of the interaction between the stress and disease epidemics may help to control the disease spread at an early stage. A mathematical model of epidemiology, specifically focusing on the vector borne diseases, with environmental stress is reported in this paper. The model is validated with the aid of stability analysis. During this research, a set of parametric values is obtained using reverse engineering. For this purpose, the parametric evaluation is reported with the help of Monte Carlo Markov Chain (MCMC) reverse engineering. Among other factors, the environmental stresses are also responsible for different dynamics of the same disease, in different continents of the world. The proposed research methodology will help in forecasting the epidemiological problems such as the current threat of coronavirus.
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    Exploration of the novel corona virus transition graphs with petrinet modeling
    (World Scientific Publishing Co Pte Ltd, 2021) Alam, Fatima; Abdel-Salam, Abdel-Salam G.; Sohail, Ayesha; Yousaf, Muhammad; Tunç, Sümeyye
    Corona virus (CoV) is a group of viruses with non-bifurcated, single-stranded, and positive-sense RNA genomes. Apart from infecting several economically significant vertebrates (such as pigs and chickens), it is reported in the recent literature that six main types of CoVs infect the human hosts and cause lung infections. In animals, CoVs cause several diseases, including pneumonia, gastrointestinal tract, and central nervous system diseases. In humans, the CoVs work as respiratory tract diseases, and the new CoVs can penetrate the barrier between other species and humans and can cause a high mortality rate. In the course of this study, a novel approach to networking, based on the density-dependent differential equations, is adopted for the precise explanation of the propagation of the virus and the effect of quarantine on it. An infectious disease model with a time delay is suggested based on the conventional infectious disease model. To describe the viral infection period and treatment time, the time differential is used. Using the epidemic data released in real-time, the minimum error is obtained firstly through the inversion of the numerical simulation parameter; then we simulate the development pattern of the epidemic according to the dynamics system; finally, the effectiveness of quarantine steps is compared and analyzed. With the help of a discrete model, the transformations are documented in detail that is difficult to evaluate numerically. The provided numerical results are in close agreement with the experimental findings. The modeling of Petri nets (PNs) used has proven to be a successful method. The current research strategy can help the public to gain awareness of the disease spread, which is highly desired.
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    Forecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process
    (Springer Heidelberg, 2022) Al-Utaibi, Khaled A.; Nutini, Alessandro; Sohail, Ayesha; Arif, Robia; Tunç, Sümeyye; Sait, Sadiq M.
    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.
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    Furin and the adaptive mutation of SARS-COV2: A computational framework
    (Springer Heidelberg, 2022) Sohail, Ayesha; Tunç, Sümeyye; Nutini, Alessandro; Arif, Robia
    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 gamma. 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.
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    Modeling and simulation of the "IL-36 cytokine" and CAR-T cells interplay in cancer onset
    (World Scientific, 2022) Al-Utaibi, Khaled Abdul-Aziz; Nutini, Alessandro; Sohail, Ayesha; Arif, Robia; Tunç, Sümeyye; Sait, Sadiq M.
    Background: CAR-T cells are chimeric antigen receptor (CAR)-T cells; they are target-specific engineered cells on tumor cells and produce T cell-mediated antitumor responses. CAR-T cell therapy is the "first-line" therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and leukemia. This adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since, precisely for this type of tumor, the use of immune checkpoint inhibitors remained disappointing. Although CAR-T is a promising therapeutic technique, there are therapeutic limits linked to the persistence of these cells and to the tumor's immune escape. CAR-T cell engineering techniques are allowed to express interleukin IL-36, and seem to be much more efficient in antitumoral action. IL-36 is involved in the long-term antitumor action, allowing CAR-T cells to be more efficient in their antitumor action due to a "cross-talk" action between the "IL-36/dendritic cells" axis and the adaptive immunity. Methods: This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of tumor. The model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy: IL-36, "armored" CAR-T cells (i.e., engineered to produce IL-36) and the tumor cell population, focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T cells. Mathematical model was developed and numerical simulations were carried out during this research. The development of the model with stability analysis by conditions of Routh-Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment, making therapy more effective against the "solid tumor". Findings: Primary malignant bone tumors are quite rare (about 3% of all tumors) and the vast majority consist of osteosarcomas and Ewing's sarcoma and, approximately, the 20% of patients undergo metastasis situations that is the most likely cause of death. Interpretation: In bone tumor like osteosarcoma, there is a variation of the cellular mechanical characteristics that can influence the efficacy of chemotherapy and increase the metastatic capacity; an approach related to adoptive immunotherapy with CAR-T cells may be a possible solution because this type of therapy is not influenced by the biomechanics of cancer cells which show peculiar characteristics.
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    Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics
    (Elsevier B.V., 2022) Yu, Zhenhua; Sohail, Ayesha; Arif, Robia; Nutini, Alessandro; Nofal, Taher A.; Tunç, Sümeyye
    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 ?, sensitivity to cytokines ? and bacterial sensitivity ?), analyzes a “threshold value” termed as the basic reproduction number R0 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.