Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient’s immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them

Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images

Rationale and Objectives: Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. MRI is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques like perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multi-parametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods, in order to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter. Materials and Methods: Five structural MR sequences, namely, B0, Diffusion Weighted Images, FLAIR, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI, i.e., fractional anisotropy and apparent diffusion coefficient, are used to create an intensity-based tissue profile. This is incorporated into a non-linear pattern classification technique to create a multi-parametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high grade neoplasms who have not received any therapy prior to imaging. Results: Preliminary results demonstrate that this multi-parametric tissue characterization helps to better differentiate between neoplasm, edema and healthy tissue, and to identify tissue that is likely progress to neoplasm in the future. This has been validated on expert assessed tissue. Conclusion: This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence

Non-invasive detection of 2-hydroxyglutarate in IDH-mutated gliomas using two-dimensional localized correlation spectroscopy (2D L-COSY) at 7 Tesla

BACKGROUND: Mutations in the isocitrate dehydrogenase enzyme are present in a majority of lower-grade gliomas and secondary glioblastomas. This mis-sense mutation results in the neomorphic reduction of isocitrate dehydrogenase resulting in an accumulation of the “oncometabolite” 2-hydroxyglutarate (2HG). Detection of 2HG can thus serve as a surrogate biomarker for these mutations, with significant translational implications including improved prognostication. Two dimensional localized correlated spectroscopy (2D L-COSY) at 7T is a highly-sensitive non-invasive technique for assessing brain metabolism. This study aims to assess tumor metabolism using 2D L-COSY at 7T for the detection of 2HG in IDH-mutant gliomas. METHODS: Nine treatment-naïve patients with suspected intracranial neoplasms were scanned at 7T MRI/MRS scanner using the 2D L-COSY technique. 2D-spectral processing and analyses were performed using a MATLAB-based reconstruction algorithm. Cross and diagonal peak volumes were quantified in the 2D L-COSY spectra and normalized with respect to the creatine peak at 3.0 ppm and quantified data were compared with previously-published data from six normal subjects. Detection of 2HG was validated using findings from immunohistochemical (IHC) staining in patients who subsequently underwent surgical resection. RESULTS: 2HG was detected in both of the IDH-mutated gliomas (grade III Anaplastic Astrocytoma and grade II Diffuse Astrocytoma) and was absent in IDH wild-type gliomas and in a patient with breast cancer metastases. 2D L-COSY was also able to resolve complex and overlapping resonances including phosphocholine (PC) from glycerophosphocholine (GPC), lactate (Lac) from lipids and glutamate (Glu) from glutamine (Gln). CONCLUSIONS: This study demonstrates the ability of 2D L-COSY to unambiguously detect 2HG in addition to other neuro metabolites. These findings may aid in establishing 2HG as a biomarker of malignant progression as well as for disease monitoring in IDH-mutated gliomas

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Healthcare, one of the most important industries, is data-oriented, but most of the research in this industry focuses on incorporating the internet of things (IoT) or connecting medical equipment. Very few researchers are looking at the data generated in the healthcare industry. Data are very important tools in this competitive world, as they can be integrated with artificial intelligence (AI) to promote sustainability. Healthcare data include the health records of patients, drug-related data, clinical trials data, data from various medical equipment, etc. Most of the data management processes are manual, time-consuming, and error-prone. Even then, different healthcare industries do not trust each other to share and collaborate on data. Distributed ledger technology is being used for innovations in different sectors including healthcare. This technology can be incorporated to maintain and exchange data between different healthcare organizations, such as hospitals, insurance companies, laboratories, pharmacies, etc. Various attributes of this technology, such as its immutability, transparency, provenance etc., can bring trust and security to the domain of the healthcare sector. In this paper, a decentralized access control model is proposed to enable the secure interoperability of different healthcare organizations. This model uses the Ethereum blockchain for its implementation. This model interfaces patients, doctors, chemists, and insurance companies, empowering the consistent and secure exchange of data. The major concerns are maintaining a history of the transactions and avoiding unauthorized updates in health records. Any transaction that changes the state of the data is reflected in the distributed ledger and can be easily traced with this model. Only authorized entities can access their respective data. Even the administrator will not be able to modify any medical records

Financing of International Collective Action for Epidemic and Pandemic Preparedness.

The global pandemic response has typically followed cycles of panic followed by neglect. We are now, once again, in a phase of neglect, leaving the world highly vulnerable to massive loss of life and economic shocks from natural or human-made epidemics and pandemics. Quantifying the size of the losses caused by large-scale outbreaks is challenging because the epidemiological and economic research in this field is still at an early stage. Research on the 1918 influenza H1N1 pandemic and recent epidemics and pandemics has shown a range of estimated losses (panel).1; 2; 3; 4; 5; 6 ; 7 A limitation in assessing the economic costs of outbreaks is that they only capture the impact on income. Fan and colleagues8 recently addressed this limitation by estimating the “inclusive” cost of pandemics: the sum of the cost in lost income and a dollar valuation of the cost of early death. They found that for Ebola and severe acute respiratory syndrome (SARS), the true (“inclusive”) costs are two to three times the income loss. For extremely serious pandemics such as that of influenza in 1918, the inclusive costs are over five times income loss. The inclusive costs of the next severe influenza pandemic could be US$570 billion each year or 0·7$1 billion over 5 years, is developing vaccines against known emerging infectious diseases as well as platforms for rapid development of vaccines against outbreaks of unknown origin. The WHO R&D Blueprint for Action to Prevent Epidemics12 is a new mechanism for coordinating and prioritising the development of drugs and diagnostics for emerging infectious diseases. Consolidating and enhancing donor support for these new initiatives would be an efficient way to channel resources aimed at improving global outbreak preparedness and response. Crucial components of the global and regional system for outbreak control include surge capacity (eg, the ability to urgently deploy human resources); providing technical guidance to countries in the event of an outbreak; and establishing a coordinated, interlinked global, regional, and national surveillance system. These activities are the remit of several essential WHO financing envelopes that all face major funding shortfalls. The Contingency Fund for Emergencies finances surge outbreak response for up to 3 months. The fund has a capitalisation target of $100 million of flexible voluntary contributions, which needs to be replenished with about$25–50 million annually, depending on the extent of the outbreak in any given year. However, as of April 30, 2017, only $37·65 million had been contributed, with an additional$4 million in pledges.13 The WHO Health Emergencies and Health Systems Preparedness Programmes face an annual shortfall of $225 million in funding their epidemic and pandemic prevention and control activities.14 Previous health emergencies have shown that it can take time to organise global collective action and provide financing to the national and local level. In such situations, a global mechanism should offer a rapid injection of liquidity to affected countries. The World Bank\u27s Pandemic Emergency Financing Facility (PEF) is a proposed global insurance mechanism for pandemic emergencies.15 It aims to provide surge funding for response efforts to help respond to rare, high-burden disease outbreaks, preventing them from becoming more deadly and costly pandemics. The PEF currently proposes a coverage of$500 million for the insurance window; increasing the current coverage will require additional donor commitments. In addition, the PEF has a $50–100 million replenishable cash window. As the world\u27s health ministers meet this month for the World Health Assembly, we propose five key ways to help prevent mortality and economic shocks from disease outbreaks. First, to accelerate development of new technologies to control outbreaks, donors should expand their financing for CEPI and support the WHO R&D Blueprint for Action to Prevent Epidemics. Second, funding gaps in the WHO Contingency Fund for Emergencies and the WHO Health Emergencies Programme should be urgently filled and the PEF should be fully financed. Third, all nations should support their own and other countries\u27 national preparedness efforts, including committing to the JEE process. Fourth, we believe it would be valuable to create and maintain a regional and country-level pandemic risk and preparedness index. This index could potentially be used as a way to review preparedness in International Monetary Fund article IV consultations (regular country reports by staff to its Board). Finally, we call for a new global effort to develop long-term national, regional, and global investment plans to create a world secure from the threat of devastation from outbreaks. This article summarises the recommendations of a workshop held at the National Academy of Medicine, Washington, DC, USA, co-hosted by the Center for Policy Impact in Global Health at Duke University, Durham, NC, USA and the Coalition for Epidemic Preparedness Innovations, Oslo, Norway. Participants\u27 travel and accommodation were supported by the Center for Policy Impact in Global Health. BO is a consultant to Metabiota, a private company engaged in infectious disease risk modelling and analytical services. In this capacity, he has led the development of an index measuring national capacity to respond to epidemic and pandemic disease outbreaks

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

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