Effective electrode positions and stimulation patterns for head EIT

The objective of this study is to find an effective stimulation and measurement strategy to improve distinguishability for head EIT. To better understand the relationship between distinguishability and various strategies (stimulation/measurement patterns) for a set of electrodes, we evaluated a realistic head model and a range of common strategies

Molecular and translational advances in meningiomas.

Meningiomas are the most common primary intracranial neoplasm. The current World Health Organization (WHO) classification categorizes meningiomas based on histopathological features, but emerging molecular data demonstrate the importance of genomic and epigenomic factors in the clinical behavior of these tumors. Treatment options for symptomatic meningiomas are limited to surgical resection where possible and adjuvant radiation therapy for tumors with concerning histopathological features or recurrent disease. At present, alternative adjuvant treatment options are not available in part due to limited historical biological analysis and clinical trial investigation on meningiomas. With advances in molecular and genomic techniques in the last decade, we have witnessed a surge of interest in understanding the genomic and epigenomic landscape of meningiomas. The field is now at the stage to adopt this molecular knowledge to refine meningioma classification and introduce molecular algorithms that can guide prediction and therapeutics for this tumor type. Animal models that recapitulate meningiomas faithfully are in critical need to test new therapeutics to facilitate rapid-cycle translation to clinical trials. Here we review the most up-to-date knowledge of molecular alterations that provide insight into meningioma behavior and are ready for application to clinical trial investigation, and highlight the landscape of available preclinical models in meningiomas

Looking beyond the hype : applied AI and machine learning in translational medicine

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale

DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management

Abstract Background Variability in standard-of-care classifications precludes accurate predictions of early tumor recurrence for individual patients with meningioma, limiting the appropriate selection of patients who would benefit from adjuvant radiotherapy to delay recurrence. We aimed to develop an individualized prediction model of early recurrence risk combining clinical and molecular factors in meningioma. Methods DNA methylation profiles of clinically annotated tumor samples across multiple institutions were used to develop a methylome model of 5-year recurrence-free survival (RFS). Subsequently, a 5-year meningioma recurrence score was generated using a nomogram that integrated the methylome model with established prognostic clinical factors. Performance of both models was evaluated and compared with standard-of-care models using multiple independent cohorts. Results The methylome-based predictor of 5-year RFS performed favorably compared with a grade-based predictor when tested using the 3 validation cohorts (ΔAUC = 0.10, 95% CI: 0.03–0.018) and was independently associated with RFS after adjusting for histopathologic grade, extent of resection, and burden of copy number alterations (hazard ratio 3.6, 95% CI: 1.8–7.2, P &lt; 0.001). A nomogram combining the methylome predictor with clinical factors demonstrated greater discrimination than a nomogram using clinical factors alone in 2 independent validation cohorts (ΔAUC = 0.25, 95% CI: 0.22–0.27) and resulted in 2 groups with distinct recurrence patterns (hazard ratio 7.7, 95% CI: 5.3–11.1, P &lt; 0.001) with clinical implications. Conclusions The models developed and validated in this study provide important prognostic information not captured by previously established clinical and molecular factors which could be used to individualize decisions regarding postoperative therapeutic interventions, in particular whether to treat patients with adjuvant radiotherapy versus observation alone. </jats:sec

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-Invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine

Advances in multidisciplinary therapy for meningiomas

Surgery has long been established as the first-line treatment for the majority of symptomatic and enlarging meningiomas, and evidence for its success is derived from retrospective case series. Despite surgical resection, a subset of meningiomas display aggressive behavior with early recurrences that are difficult to treat. The decision to radically resect meningiomas and involved structures is balanced against the risk for neurological injury in patients. Radiation therapy has largely been used as a complementary and safe therapeutic strategy in meningiomas with evidence primarily stemming from retrospective, single-Institution reports. Two of the first cooperative group studies (RTOG 0539 and EORTC 22042) evaluating the outcomes of adjuvant radiation therapy in higher-risk meningiomas have shown promising preliminary results. Historically, systemic therapy has resulted in disappointing results in meningiomas. However, several clinical trials are under way evaluating the efficacy of chemotherapies, such as trabectedin, and novel molecular agents targeting Smoothened, AKT1, and focal adhesion kinase in patients with recurrent meningiomas

Impact of natural antioxidant systems on the oxidation resistance and mechanical properties of polypropylene

This paper describes the separation of oxidation resistant components from the seeds of pomegranate (PSA), grape (GSE) and sea buckthorn (SSE). The anti-oxidation properties of the resultant extracts, used as the natural anti-oxidants for polypropylene (PP), were compared with Irganox1010. The effects of these natural antioxidants on the antioxidant levels of PP samples were estimated by thermal oxidative aging and micromixed rheology, OIT, XRD, SEM, TEM and mechanical properties tests of samples before and after aging. The results show that adding PSA, GSE and SSE can obviously increase the mechanical properties of PP. In addition, the molding stability of polypropylene raw material is prolonged and improved. Moreover, the mechanical properties of the PP samples after 240 h of thermal oxidative aging indicates that, the best results, closest to the anti-oxidation ability of Irganox1010, can be obtained when the additive amount is 0.5% (wt%) for PSE or 0.7% (wt%) for GSE