Microfluidic glucose biosensors and potassium ion selective electrodes were used in an in vivo study to measure the neurochemical effects of spreading depolarizations (SD), which have been shown to be detrimental to the injured human brain. A microdialysis probe implanted in the cortex of rats was connected to a microfluidic PDMS chip containing the sensors. The dialysate was also analyzed using our gold standard, rapid sampling microdialysis (rsMD). The glucose biosensor performance was validated against rsMD with excellent results. The glucose biosensors successfully monitored concentration changes, in response to SD wave induction, in the range of 10–400 μM with a second time-resolution. The data show that during a SD wave, there is a time delay of 62 ± 24.8 s (n = 4) between the onset of the increase in potassium and the decrease in glucose. This delay can be for the first time demonstrated, thanks to the high-temporal resolution of the microfluidic sensors sampling from a single tissue site (the microdialysis probe), and it indicates that the decrease in glucose is due to the high demand of energy required for repolarization.

Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2-mutant, IDH1/2-mutant with TERT promoter mutation, and IDH-wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.

Background. The purpose of this study was to test the hypothesis that the genetic backgrounds of lung cancers could affect the spatial distribution of brain metastases.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

We analyzed the metabolic response to cortical spreading depression that drastically increases local energy demand to restore ion homeostasis. During single and multiple cortical spreading depressions in the rat cortex, we simultaneously monitored extracellular levels of glucose and lactate using rapid sampling microdialysis and glucose influx using 18 F-fluorodeoxyglucose positron emission tomography while tracking cortical spreading depression using laser speckle imaging. Combining the acquired data with steady-state requirements we developed a mass-conserving compartment model including neurons and glia that was consistent with the observed data. In summary, our findings are: (1) Early breakdown of glial glycogen provides a major source of energy during increased energy demand and leaves 80% of blood-borne glucose to neurons. (2) Lactate is used solely by neurons and only if extracellular lactate levels are >80% above normal. (3) Although the ratio of oxygen and glucose consumption transiently reaches levels <3, the major part (>90%) of the overall energy supply is from oxidative metabolism. (4) During cortical spreading depression, brain release of lactate exceeds its consumption suggesting that lactate is only a circumstantial energy substrate. Our findings provide a general scenario for the metabolic response to increased cerebral energy demand.

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Regional cerebral blood flow (rCBF) is spatially and temporally adjusted to local energy needs. This coupling involves dilation of vessels both at the site of metabolite exchange and upstream of the activated region. Deficits in upstream blood supply limit the ‘capacity to raise rCBF' in response to functional activation and therefore compromise brain function. We here demonstrate in rats that the ‘capacity to raise rCBF' can be determined from real-time measurements of rCBF using laser speckle imaging during an energy challenge induced by cortical spreading depolarizations (CSDs). Cortical spreading depolarizations (CSDs) occur with high incidence in stroke and various other brain injuries and cause large metabolic changes. Various conditions of cerebral perfusion were induced, either by modifying microvascular tone, or by altering upstream blood supply independently. The increase in rCBF per unit of time in response to CSD was linearly correlated to the upstream blood supply. In an experimental model of stroke, we found that this marker of the capacity to raise rCBF which, in pathologic tissue may be additionally limited by impaired vasoactive signaling, was a better indicator of the functional status of cerebral tissue than local rCBF levels.

Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge1–5. Here we conducted a genome-wide association study (GWAS) involving 2,393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3,289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target.