with a 1-day history of fever without dizziness, cough, and headaches. On presentation, his temperature was 38•1°C. Laboratory tests showed a C-reactive protein concentration of 0•56 mg/dL (normal range 0•00-0•60] mg/dL). Complete blood count showed elevated leukocytes (10 060 cells per μL [normal range 3500-9500 cells per μL]), neutrophils (7550 cells per μL [1800-6300 cells per μL]), and monocytes (990 cells per μL [100-600 cells per μL]), while the lymphocyte count (1490 cells per μL) was in the normal range (1100-3200 cells per μL). The patient was negative for influenza A and B viruses, adenovirus, respiratory syncytial virus, and parainfluenza 1, 2, and 3 viruses. Chest CT showed multiple ground-glass opacities in the lower lobes bilaterally.The patient was given antibacterial, antiviral, and corticosteroid treatments (moxifloxacin [0•4 g/day] for 5 days, followed by ribavirin [0•5 g/day] and methylprednisolone [40 mg/day] for 5 days) via intravenous drop infusion. However, after 10 days, the patient had persistent fever (highest temperature 38•5°C), cough, and shortness of breath. The patient was diagnosed with coronavirus Contributors CZ and CG contributed to data analysis, data interpretation, the literature search, and manuscript drafting. YX contributed to data collection, data analysis, and figure preparation. MX contributed to study design and reviewed the final draft. All authors read and approved the manuscript.

The paravascular pathway, also known as the “glymphatic” pathway, is a recently described system for waste clearance in the brain. According to this model, cerebrospinal fluid (CSF) enters the paravascular spaces surrounding penetrating arteries of the brain, mixes with interstitial fluid (ISF) and solutes in the parenchyma, and exits along paravascular spaces of draining veins. Studies have shown that metabolic waste products and solutes, including proteins involved in the pathogenesis of neurodegenerative diseases such as amyloid-beta, may be cleared by this pathway. Consequently, a growing body of research has begun to explore the association between glymphatic dysfunction and various disease states. However, significant controversy exists in the literature regarding both the direction of waste clearance as well as the anatomical space in which the waste-fluid mixture is contained. Some studies have found no evidence of interstitial solute clearance along the paravascular space of veins. Rather, they demonstrate a perivascular pathway in which waste is cleared from the brain along an anatomically distinct perivascular space in a direction opposite to that of paravascular flow. Although possible explanations have been offered, none have been able to fully reconcile the discrepancies in the literature, and many questions remain. Given the therapeutic potential that a comprehensive understanding of brain waste clearance pathways might offer, further research and clarification is highly warranted.

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et al. (2019) Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. European Radiology.

Background: The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods: Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results: The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions: Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

The main objective of this study was to utilize high field (7T) in vivo proton magnetic resonance imaging to increase the ability to detect metabolite changes in people with ALS, specifically, to quantify levels of glutamine and glutamine separately. The second objective of this study was to correlate metabolic markers with clinical outcomes of disease progression. 13 ALS participants and 12 age-matched healthy controls (HC) underwent 7 Tesla MRI and MRS. Single voxel MR spectra were acquired from the left precentral gyrus using a very short echo time (TE = 5 ms) STEAM sequence. MRS data was quantified using LCModel and correlated to clinical outcome markers. N-acetylaspartate (NAA) and total NAA (tNA, NAA + NAAG) were decreased by 17% in people with ALS compared to HC (P = 0.004 and P = 0.005, respectively) indicating neuronal injury and/or loss in the precentral gyrus. tNA correlated with disease progression as measured by forced vital capacity (FVC) (P = 0.014; Rρ = 0.66) and tNA/tCr correlated with overall functional decline as measured by worsening of the ALS Functional Rating Scale-Revised (ALSFRS-R) (P = 0.004; Rρ = -0.74). These findings underscore the importance of NAA as a reliable biomarker for neuronal injury and disease progression in ALS. Glutamate (Glu) was 15% decreased in people with ALS compared to HC (P = 0.02) while glutamine (Gln) concentrations were similar between the two groups. Furthermore, the decrease in Glu correlated with the decrease in FVC (P = 0.013; Rρ = 0.66), a clinical marker of disease progression. The decrease in Glu is most likely driven by intracellular Glu loss due to neuronal loss and degeneration. Neither choline containing components (Cho), a marker for cell membrane turnover, nor myo-Inositol (mI), a suspected marker for neuroinflammation, showed significant differences between the two groups. However, mI/tNA was correlated with upper motor neuron burden (P = 0.004, Rρ = 0.74), which may reflect a relative increase of activated microglia around motor neurons. In summary, 7T 1H MRS is a powerful non-invasive imaging technique to study molecular changes related to neuronal injury and/or loss in people with ALS.

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Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal Zhang et al. MRI-Radiomic of Prostate Cancer validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.