Prognostic Value of BRAF Mutation in Glioblastoma.

With advances in molecular genetics, exploring targetable mutations for treating glioblastoma (GBM) patients, has become a centre of interest in modern day neuropathology. BRAF mutation has been extensively reported in several brain tumours. Recent studies report identification of BRAF mutation in GBM patients, especially isocitrate dehydrogenase wildtype glioblastomas (IDH-WT GBM), and its potential role in patient outcomes. Here we discuss the existing literature on the prognostic value of BRAF mutation in GBM.

Spinal meningiomas: Management and outcomes.

Spinal meningiomas are relatively rare, benign, intradural, extramedullary tumours, that are typically slow-growing and well-defined. Surgery is always the first line for treating spinal meningiomas. Here, we have discussed the existing literature on spinal meningiomas and the role of surgery in determining the outcomes.

Spinal Schwannomas; Classification, Management And Outcomes.

Schwannomas are benign tumours of the peripheral nerve sheath. When they occur in spine, they are most commonly found in intradural-extramedullary location. Surgery is the mainstay of treatment. Radiation has a limited role in the management of residual or recurrent lesions not suitable for surgery. Here we discuss the existing literature on the outcomes of spinal schwannoma after surgery.

Adverse effects of methylene blue in peripheral neurons: An in vitro electrophysiology and cell culture study.

Methylene blue (MB) is an effective treatment for methemoglobinemia, ifosfamide-induced encephalopathy, cyanide poisoning, and refractory vasoplegia. However, clinical case reports and preclinical studies indicate potentially neurotoxic activity of MB at certain concentrations. The exact mechanisms of MB neurotoxicity are not known, and while the effects of MB on neuronal tissue from different brain regions and myenteric ganglia have been examined, its effects on primary afferent neurons from dorsal root ganglia (DRG) have not been studied. Mouse DRG were exposed to MB (0.3-10 µM) in vitro to assess neurite outgrowth. Increasing concentrations of MB (0.3-10 µM) were associated with neurotoxicity as shown by a substantial loss of cells with neurite formation, particularly at 10 µM. In parallel experiments, cultured rat DRG neurons were treated with MB (100 µM) to examine how MB affects electrical membrane properties of small-diameter sensory neurons. MB decreased peak inward and outward current densities, decreased action potential amplitude, overshoot, afterhyperpolarization, increased action potential rise time, and decreased action potential firing in response to current stimulation. MB induced dose-dependent toxicity in peripheral neurons, in vitro. These findings are consistent with studies in brain and myenteric ganglion neurons showing increased neuronal loss and altered membrane electrical properties after MB application. Further research is needed to parse out the toxicity profile for MB to minimize damage to neuronal structures and reduce side effects in clinical settings.

Self organizing maps for the parametric analysis of COVID-19 SEIRS delayed model.

Since 2019, entire world is facing the accelerating threat of Corona Virus, with its third wave on its way, although accompanied with several vaccination strategies made by world health organization. The control on the transmission of the virus is highly desired, even though several key measures have already been made, including masks, sanitizing and disinfecting measures. The ongoing research, though devoted to this pandemic, has certain flaws, due to which no permanent solution has been discovered. Currently different data based studies have emerged but unfortunately, the pandemic fate is still unrevealed. During this research, we have focused on a compartmental model, where delay is taken into account from one compartment to another. The model depicts the dynamics of the disease relative to time and constant delays in time. A deep learning technique called "Self Organizing Map" is used to extract the parametric values from the data repository of COVID-19. The input we used for SOM are the attributes on which, the variables are dependent. Different grouping/clustering of patients were achieved with 2- dimensional visualization of the input data ( h t t p s : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y / 2.0 / ). Extensive stability analysis and numerical results are presented in this manuscript which can help in designing control measures.

Forecasting the impact of environmental stresses on the frequent waves of COVID19.

A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of "nonlinear autoregressive network with exogenous inputs (NARX)" approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of COVID-19, outdoor air pollution is unlikely to be a significant route of transmission. In this research, ANN time series is used to analyze the data resulting from the COVID-19 first and second waves and the forecasted results show that air pollution affects people in different areas of Italy and make more people sick with covid-19. The model is developed based on the disease transmission data of Italy.

Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection.

It is highly desired to explore the interventions of COVID-19 for early treatment strategies. Such interventions are still under consideration. A model is benchmarked research and comprises target cells, virus infected cells, immune cells, pro-inflammatory cytokines, and, anti-inflammatory cytokine. The interaction of the drug with the inflammatory sub-system is analyzed with the aid of kinetic modeling. The impact of drug therapy on the immune cells is modelled and the computational framework is verified with the aid of numerical simulations. The work includes a significant hypothesis that quantifies the complex dynamics of the infection, by relating it to the effect of the inflammatory syndrome generated by IL-6. In this paper we use the cancer immunoediting process: a dynamic process initiated by cancer cells in response to immune surveillance of the immune system that it can be conceptualized by an alternating movement that balances immune protection with immune evasion. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this process the immune system can both constrain and promote tumour development, which proceeds through three phases termed: (i) Elimination, (ii) Equilibrium, and, (iii) Escape [1]. We can also apply these concepts to viral infection, which, although it is not exactly "immunoediting", has many points in common and helps to understand how it expands into an "untreated" host and can help in understanding the SARS-CoV2 virus infection and treatment model.

A mathematical modelling approach for treatment and control of Echinococcus multilocularis.

Alveolar echinococcosis (AE) is a zoonotic parasitic diseases caused by a cestode parasite known as Echinococcus multilocularis. The parasite has a wildlife cycle with definitive hosts (foxes) and small mammals as intermediate hosts (rodents) while humans are the accidental hosts. Parasite infection pressure relation to time of the year and age dependent infection pressure for parasite abundance also depend on the urbanization. The aim of current work is forecasting the thresholds via the computational analysis of the disease spread which is a useful approach since it can help to design the experimental settings with better planning and efficiency. Network analysis when interlinked with the computational techniques provides better insight into the spatial and temporal heterogeneities. In the present study, a mathematical framework that describes the transmission dynamics and control measures of E. multilocularis in foxes is documented. We used treatment of foxes with baits for the prevention of the E. multilocularis infection. A novel approach of networking, called Petri net (PN), based on density dependent differential equations, is utilized during this research. The accurate description of the transmission of the parasite and the effect of drug on it is provided to the readers in this article. The transitions, which are difficult to analyse theoretically, are presented with the aid of the discrete approach of networking. A discrete mathematical framework can prove to be an accurate and robust tool to analyse and control the parasite dynamics.

Is endoscopic third ventriculostomy a viable treatment option for normal pressure hydrocephalus? A systematic review.

Background: Endoscopic third ventriculostomy (ETV) is considered an alternative treatment for hydrocephalus and has become a standard of care for obstructive hydrocephalus. Recent studies have also explored its role in normal pressure hydrocephalus (NPH). We conducted a systematic review aiming to assess the outcomes of this minimally invasive endoscopic technique as a viable treatment option for NPH. Methods: A systematic literature search was performed using PubMed and Scopus databases, using iterations of search terms "Endoscopic third ventriculostomy," "Idiopathic normal pressure hydrocephalus," and "Normal pressure hydrocephalus." To be eligible for inclusion in the review, articles had to report the usage of ETV as a primary treatment modality for NPH, report its outcomes, and be published in the English language. Results: Out of the 13 studies selected for qualitative synthesis, nine supported the use of ETV for NPH as an effective treatment option with improvement in the preoperative symptoms. Two studies favored shunt over ETV, stating that quality of life is better with VP shunt insertion. One study reported that ETV has higher perioperative mortality rates that outweigh its benefits. One study reported it to be an ineffective surgical option. Conclusion: The current review of evidence does not support the use of ETV for the treatment of NPH, except perhaps in a small subset of patients. These patients have a shorter duration of symptoms and a better preoperative neurological status. The lumbar infusion test and ventricular infusion test are modalities useful for selecting these candidates.

Optimizing the dynamics of bone turnover with genetic algorithm.

Control systems and the modeling strategies are not only limited to engineering problems. These approaches can be used in the field of bio-mathematics as well and modern studies have promoted this approach to a great extent. The computational modeling and simulation of bone metastasis is painful yet critical after cancer invades the body. This vicious cycle is complex, and several research centers worldwide are devoted to understanding the dynamics and setting up a treatment strategy for this life-threatening behavior of cancer. Cancerous cells activation and the corresponding process of metastasis is reported to boost during the periodic waves of COVID-19, due to the inflammatory nature of the infection associated with SARS-2 and its variants. The bone cells are comprised of two types of cells responsible for bone formation and resorption. The computational framework of such cells, in spatial form, can help the researchers forecast the bone dynamics in a robust manner where the impact of cancer is incorporated into the computational model as a source of perturbation. A series of computational models are presented to explore the complex behavior of bone metastasis with COVID-19 induced infection. The finite difference algorithm is used to simulate the nonlinear computational model. The results obtained are in close agreement with the experimental findings. The computational results can help explore the vicious cycle's fate and help set up control strategies through drug therapies.

COVID-19 Variants and Transfer Learning for the Emerging Stringency Indices.

The pandemics in the history of world health organization have always left memorable hallmarks, on the health care systems and on the economy of highly effected areas. The ongoing pandemic is one of the most harmful pandemics and is threatening due to its transformation to more contiguous variants. Here in this manuscript, we will first outline the variants and then their impact on the associated health issues. The deep learning algorithms are useful in developing models, from a higher dimensional problem/ dataset, but these algorithms fail to provide insight during the training process and do not generalize the conditions. Transfer learning, a new subfield of machine learning has acquired fame due to its ability to exploit the information/learning gained from a previous process to improve generalization for the next. In short, transfer learning is the optimization of the stored knowledge. With the aid of transfer learning, we will show that the stringency index and cardiovascular death rates were the most important and appropriate predictors to develop the model for the forecasting of the COVID-19 death rates.

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.

Forecasting of the efficiency of monoclonal therapy in the treatment of CoViD-19 induced by the Omicron variant of SARS-CoV2.

On November 26, 2021, the World Health Organization (WHO) announced a new variant of concern of SARS-CoV2 called Omicron. This variant has biological-functional characteristics such as to make it much faster in the infectious process so as to show an even more intense spread. Although many data are currently incomplete, it is possible to identify, based on the viral biochemical characteristics, a possible therapy consisting of a monoclonal antibody called Sotrovimab. The model proposed here is based on the mathematical analysis of the dynamics of action of this monoclonal antibody and two cell populations: the immune memory cells and the infected cells. Indeed, a delay exists during the physiological immune response and the response induced by administration of Sotrovimab. This manuscript presents that delay in a novel manner. The model is developed with the aid of information based on the chemical kinetics. The machine learning tools have been used to satisfy the criteria designed by the dynamical analysis. Regression learner tools of Python are used as the reverse engineering tools for the understanding of the balance in the mathematical model, maintained by the parameters and their corresponding intervals and thresholds set by the dynamical analysis.

Piecewise differentiation of the fractional order CAR-T cells-SARS-2 virus model.

The pandemic caused by the SARS-CoV2 virus has prompted research into new therapeutic solutions that can be used to treat the CoVid-19 syndrome. As part of this research, immunotherapy, first developed against cancer, is offering new therapeutic horizons also against viral infections. CAR technology, with the production of CAR-T cells (adoptive immunotherapy), has shown applicability in the field of HIV viral infections through second generation CAR-T cells implemented with the "CD4CAR" system with a viral fusion inhibitor. In addition, to avoid the immunoescape of the virus, bi- or trispecific CAR receptors have been developed. Our research group hypothesizes the use of this immunotherapy system against SARS-CoV2, admitting the appropriate adjustments concerning the target-epitope and a possible remodeling of the nuclease related to the action of this virus. For a more in-depth analysis of this hypothesis, a mathematical model has been developed which, starting from the fractional derivative Caputo, creates a system of equations that describes the interactions between CAR-T cells, memory cells, and cells infected with SARS-CoV2. Through an analysis of the existence and non-negativity of the solutions, the hypothesis is stabilized; then is further demonstrated through the use of the piece-wise derivative and the consequent application of the formula of Newton polynomial interpolation.

Fractional boundary element solution of three-temperature thermoelectric problems.

The primary goal of this article is to propose a new fractional boundary element technique for solving nonlinear three-temperature (3 T) thermoelectric problems. Analytical solution of the current problem is extremely difficult to obtain. To overcome this difficulty, a new numerical technique must be developed to solve such problem. As a result, we propose a novel fractional boundary element method (BEM) to solve the governing equations of our considered problem. Because of the advantages of the BEM solution, such as the ability to treat problems with complicated geometries that were difficult to solve using previous numerical methods, and the fact that the internal domain does not need to be discretized. As a result, the BEM can be used in a wide variety of thermoelectric applications. The numerical results show the effects of the magnetic field and the graded parameter on thermal stresses. The numerical results also validate the validity and accuracy of the proposed technique.

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.

Computational model to explore the endocrine response to trastuzumab action in HER-2/neu positive breast cancer.

Breast cancer is a very frequent type of cancer and much attention is paid to therapy with considerable efforts both in the pharmacological and clinical fields.The present work aims to create a non-linear dynamic model of action of the drug Trastuzumab against HER-2 + breast cancer, mainly considering its action of ADCP (antibody-dependent phagocytosis) killing of cancer cells. The model, while also considering the other therapeutic effects induced by Trastuzumab, shows how the action of this monoclonal antibody in the induction of ADCP through the action of macrophages, is strictly connected to the formation of a multi-complex "Trastuzumab -HER-2 - macrophage" that shows a prolonged action over time, responsible for the increase in the Overall Survivor (OS) parameter reported in various. The model shows the correlation between the various therapeutic effects and the killing action of cancer cells through the variation of the dynamic fluctuation of the representative "c" parameter.

Heat induction in two-dimensional graphene-Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling.

We report the synthesis and characterization of graphene functionalized with iron (Fe3+) oxide (G-Fe3O4) nanohybrids for radio-frequency magnetic hyperthermia application. We adopted the wet chemical procedure, using various contents of Fe3O4 (magnetite) from 0-100% for making two-dimensional graphene-Fe3O4 nanohybrids. The homogeneous dispersal of Fe3O4 nanoparticles decorated on the graphene surface combined with their biocompatibility and high thermal conductivity make them an excellent material for magnetic hyperthermia. The morphological and magnetic properties of the nanohybrids were studied using scanning electron microscopy (SEM) and a vibrating sample magnetometer (VSM), respectively. The smart magnetic platforms were exposed to an alternating current (AC) magnetic field of 633 kHz and of strength 9.1 mT for studying their hyperthermic performance. The localized antitumor effects were investigated with artificial neural network modeling. A neural net time-series model was developed for the assessment of the best nanohybrid composition to serve the purpose with an accuracy close to 100%. Six Nonlinear Autoregressive with External Input (NARX) models were obtained, one for each of the components. The assessment of the accuracy of the predicted results has been done on the basis of Mean Squared Error (MSE). The highest Mean Squared Error value was obtained for the nanohybrid containing 45% magnetite and 55% graphene (F45G55) in the training phase i.e., 0.44703, which is where the model achieved optimal results after 71 epochs. The F45G55 nanohybrid was found to be the best for hyperthermia applications in low dosage with the highest specific absorption rate (SAR) and mean squared error values.

Supervised and unsupervised algorithms for bioinformatics and data science.

Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsupervised techniques is presented with the aid of examples. The aim of this article is to provide the readers with the basic understanding of the state of the art models, which are key ingredients of explainable machine learning in the field of bioinformatics.