Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Abstract: Background: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods: Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results: Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion: Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance

Efferocytosis in dendritic cells: an overlooked immunoregulatory process

Efferocytosis, the process of engulfing and removing apoptotic cells, plays an essential role in preserving tissue health and averting undue inflammation. While macrophages are primarily known for this task, dendritic cells (DCs) also play a significant role. This review delves into the unique contributions of various DC subsets to efferocytosis, highlighting the distinctions in how DCs and macrophages recognize and handle apoptotic cells. It further explores how efferocytosis influences DC maturation, thereby affecting immune tolerance. This underscores the pivotal role of DCs in orchestrating immune responses and sustaining immune equilibrium, providing new insights into their function in immune regulation

Current-driven magnetization switching in a van der Waals ferromagnet Fe3GeTe2

The recent discovery of ferromagnetism in two-dimensional (2D) van der Waals (vdW) materials holds promises for novel spintronic devices with exceptional performances. However, in order to utilize 2D vdW magnets for building spintronic nanodevices such as magnetic memories, key challenges remain in terms of effectively switching the magnetization from one state to the other electrically. Here, we devise a bilayer structure of Fe3GeTe2/Pt, in which the magnetization of few-layered Fe3GeTe2 can be effectively switched by the spin-orbit torques (SOTs) originated from the current flowing in the Pt layer. The effective magnetic fields corresponding to the SOTs are further quantitatively characterized using harmonic measurements. Our demonstration of the SOT-driven magnetization switching in a 2D vdW magnet could pave the way for implementing low-dimensional materials in the next-generation spintronic applications

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Abstract: Background: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods: Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results: Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion: Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance

Effect of Wind-Induced Vibration on Measurement Range of Microcantilever Anemometer

In this paper, the effect of wind-induced vibration on measurement range of microcantilever anemometer is investigated for the first time. The microcantilever anemometer is composed of a flexible substrate and a piezoresistor. The wind speed can be detected through the airflow-induced deformation in the flexible substrate. Previous work indicated that the flexible substrate vibrates violently once the wind speed exceeds a critical value, resulting in severe output jitter. This wind-induced vibration limits the measurement range of the anemometer, and the relationship between the anemometer measurement range and its structural parameters has not been explored systematically. Therefore, this paper aims to reveal this relationship theoretically and experimentally, demonstrating that a shorter and thicker cantilever with larger stiffness can effectively suppress the wind-induced vibration, leading to the critical speed rising. By eliminating the wind-induced vibration, the measurement range of the microcantilever anemometer can be increased by up to 697%. These results presented in this paper can pave the way for the design and fabrication of wide-range mechanical anemometers

Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review

Background and purpose: Despite multimodal treatment of glioblastoma, recurrence beyond the initial tumor volume is inevitable. Moreover, conventional MRI has shortcomings that hinder the early detection of occult white matter tract infiltration by tumor, but diffusion tensor imaging (DTI) is a sensitive probe for assessing microstructural changes, facilitating the identification of progression before standard imaging. This sensitivity makes DTI a valuable tool for predicting recurrence. A systematic review was therefore conducted to investigate how DTI, in comparison to conventional MRI, can be used for predicting glioblastoma progression. Methods: We queried three databases (PubMed, Web of Science, and Scopus) using the search terms: (diffusion tensor imaging OR DTI) AND (glioblastoma OR GBM) AND (recurrence OR progression). For included studies, data pertaining to the study type, number of glioblastoma recurrence patients, treatment type(s), and DTI-related metrics of recurrence were extracted. Results: In all, 16 studies were included, from which there were 394 patients in total. Six studies reported decreased fractional anisotropy in recurrence regions, and 2 studies described the utility of connectomics/tractography for predicting tumor migratory pathways to a site of recurrence. Three studies reported evidence of tumor progression using DTI before recurrence was visible on conventional imaging. Conclusions: These findings suggest that DTI metrics may be useful for guiding surgical and radiotherapy planning for glioblastoma patients, and for informing long-term surveillance. Understanding the current state of the literature pertaining to these metrics’ trends is crucial, particularly as DTI is increasingly used as a treatment-guiding imaging modality.National Institute for Health and Care Research (NIHR), Career Development Fellowship (CDF-2018-11-ST2-003) NIHR HealthTech Research Centre in Brain Injury NIHR Cambridge Biomedical Research Centre (NIHR203312) Amma Kyei-Mensah Medical Scholarship Stamps Scholarshi

Anomalous circularly polarized light emission in organic light-emitting diodes caused by orbital-momentum locking

Chiral circularly polarized (CP) light is central to many photonic technologies, from optical communication of spin information to novel display and imaging technologies. As such, there has been significant effort in the development of chiral emissive materials that allow for the emission of strongly dissymmetric CP light from organic light-emitting diodes (OLEDs). A consensus for chiral emission in such devices is that the molecular chirality of the active layer determines the favored light handedness of CP emission, regardless of the light-emitting direction. Here, we discover that, unconventionally, oppositely propagating CP light exhibits opposite handedness and reversing the current-flow in OLEDs also switches the handedness of the emitted CP light. This direction-dependent CP emission boosts the net polarization rate by orders of magnitude by resolving an established issue in CP-OLEDs, where the CP light reflected by the back electrode typically erodes the measured dissymmetry. Through detailed theoretical analysis, we assign this anomalous CP emission to a ubiquitous topological electronic property in chiral materials, namely the orbital-momentum locking. Our work paves the way to design new chiroptoelectronic devices and probes the close connections between chiral materials, topological electrons, and CP light in the quantum regime

Molecular diversity in isocitrate dehydrogenase-wild-type glioblastoma.

Funder: National Institute for Health and Care Research; DOI: https://doi.org/10.13039/501100000272Funder: Brain Injury MedTech Co-operativeFunder: National Health ServiceFunder: Department of Health and Social Care; DOI: https://doi.org/10.13039/501100000276In the dynamic landscape of glioblastoma, the 2021 World Health Organization Classification of Central Nervous System tumours endeavoured to establish biological homogeneity, yet isocitrate dehydrogenase-wild-type (IDH-wt) glioblastoma persists as a tapestry of clinical and molecular diversity. Intertumoural heterogeneity in IDH-wt glioblastoma presents a formidable challenge in treatment strategies. Recent strides in genetics and molecular biology have enhanced diagnostic precision, revealing distinct subtypes and invasive patterns that influence survival in patients with IDH-wt glioblastoma. Genetic and molecular biomarkers, such as the overexpression of neurofibromin 1, phosphatase and tensin homolog and/or cyclin-dependent kinase inhibitor 2A, along with specific immune cell abundance and neurotransmitters, correlate with favourable outcomes. Conversely, increased expression of epidermal growth factor receptor tyrosine kinase, platelet-derived growth factor receptor alpha and/or vascular endothelial growth factor receptor, coupled with the prevalence of glioma stem cells, tumour-associated myeloid cells, regulatory T cells and exhausted effector cells, signifies an unfavourable prognosis. The methylation status of O6-methylguanine-DNA methyltransferase and the influence of microenvironmental factors and neurotransmitters further shape treatment responses. Understanding intertumoural heterogeneity is complemented by insights into intratumoural dynamics and cellular interactions within the tumour microenvironment. Glioma stem cells and immune cell composition significantly impact progression and outcomes, emphasizing the need for personalized therapies targeting pro-tumoural signalling pathways and resistance mechanisms. A successful glioblastoma management demands biomarker identification, combination therapies and a nuanced approach considering intratumoural variability. These advancements herald a transformative era in glioblastoma comprehension and treatment