Coronavirus Disease 2019 is predominantly a disorder of the respiratory system, but neurological complications have been recognised since early in the pandemic. The major pathophysiological processes leading to neurological damage in COVID-19 are cerebrovascular disease, immunologically mediated neurological disorders and the detrimental effects of critical illness on the nervous system. It is still unclear whether direct invasion of the nervous system by the Severe Acute Respiratory Syndrome Coronavirus 2 occurs; given the vast numbers of people infected at this point, this uncertainty suggests that nervous system infection is unlikely to represent a significant issue if it occurs at all. In this review, we explore what has been learnt about the neurological complications of COVID-19 over the course of the pandemic, and by which mechanisms these complications most commonly occur.

COVID-19 is associated with neurological complications including stroke, delirium and encephalitis. Furthermore, a post-viral syndrome dominated by neuropsychiatric symptoms is common, and is seemingly unrelated to COVID-19 severity. The true frequency and underlying mechanisms of neurological injury are unknown, but exaggerated host inflammatory responses appear to be a key driver of COVID-19 severity. We investigated the dynamics of, and relationship between, serum markers of brain injury (neurofilament light [NfL], glial fibrillary acidic protein [GFAP] and total tau) and markers of dysregulated host response (autoantibody production and cytokine profiles) in 175 patients admitted with COVID-19 and 45 patients with influenza. During hospitalisation, sera from patients with COVID-19 demonstrated elevations of NfL and GFAP in a severity-dependent manner, with evidence of ongoing active brain injury at follow-up 4 months later. These biomarkers were associated with elevations of pro-inflammatory cytokines and the presence of autoantibodies to a large number of different antigens. Autoantibodies were commonly seen against lung surfactant proteins but also brain proteins such as myelin associated glycoprotein. Commensurate findings were seen in the influenza cohort. A distinct process characterised by elevation of serum total tau was seen in patients at follow-up, which appeared to be independent of initial disease severity and was not associated with dysregulated immune responses unlike NfL and GFAP. These results demonstrate that brain injury is a common consequence of both COVID-19 and influenza, and is therefore likely to be a feature of severe viral infection more broadly. The brain injury occurs in the context of dysregulation of both innate and adaptive immune responses, with no single pathogenic mechanism clearly responsible

Background: Electronic health records represent a large data source for outcomes research, but the majority of EHR data is unstructured (e.g. free text of clinical notes) and not conducive to computational methods. While there are currently approaches to handle unstructured data, such as manual abstraction, structured proxy variables, and model-assisted abstraction, these methods are time-consuming, not scalable, and require clinical domain expertise. This paper aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. Methods: We trained selective prediction models to identify the presence of four distinct clinical variables in free-text pathology reports: primary cancer diagnosis of glioblastoma (GBM, n = 659), resection of rectal adenocarcinoma (RRA, n = 601), and two procedures for resection of rectal adenocarcinoma: abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601). Data were manually abstracted from pathology reports and used to train L1-regularized logistic regression models using term-frequency-inverse-document-frequency features. Data points that the model was unable to predict with high certainty were manually abstracted. Findings: All four selective prediction models achieved a test-set sensitivity, specificity, positive predictive value, and negative predictive value above 0.91. The use of selective prediction led to sizable gains in automation (anywhere from 57% to 95% reduction in manual abstraction of charts across the four outcomes). For our GBM classifier, the selective prediction model saw improvements to sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier. Interpretation: Selective prediction using utility-based probability thresholds can facilitate unstructured data extraction by giving "easy" charts to a model and "hard" charts to human abstractors, thus increasing efficiency while maintaining or improving accuracy.

Key Points Question What are common health care utilization patterns for management of newly diagnosed acute neck pain and how are they associated with care delivery? Findings In this cross-sectional study of 679 030 adults conducted between 2008 and 2015, early imaging was common and often occurred before conservative treatment. However, early conservative therapy before imaging was associated with lower health care costs and reduced opioid use. Meaning These findings suggest a need for improved care standardization with prompt conservative therapy initiation to reduce treatment cost and increase effectiveness.

Aroma substances are the most crucial criteria for the sensory evaluation of tea quality, and also key attractors influencing consumers to make the decision for purchasing tea. Understanding the aromatic properties of tea infusion during different brewing time is crucial to control the tea aromatic quality. Here, headspace and direct immersion solid-phase microextraction (HS-SPME and DI-SPME), coupled with GC-MS, were employed to investigate the impact of brewing time on the changes of the volatile features of green tea infusion. Esters, aldehydes, alcohols, fatty acids, and alkaloids were the predominant volatile groups from tea infusions. Two to three minutes was identified as the best duration for the tea brewing that can maximize the abundance of aromatic chemicals in the headspace emitted from the tea infusions. The variation of the key aromatic contributors between the tea infusion and the headspace over the infusion tended to equilibrate during the tea brewing process. This study provides a theory-based reference method by analyzing the real-time aromatic characteristics in green tea. The optimal time was determined for aromatic quality control, and the complementary relationship between the volatiles in the headspace and its counterpart, tea infusion, was primarily elucidated.

Background and Objective: Immunotherapy has yielded significant improvements in survival for many cancer types, but its impact on glioblastoma (GBM) has been relatively muted. There is a growing interest in understanding the role of cancer metabolism and its role in tumor growth and therapeutic response. Thus, it is equally important to consider the clinical implications of immune cell metabolism on cancer progression and implications for therapeutic development. Our objective is to present new developments in immunometabolic research that are relevant to immunotherapy development for high-grade gliomas.Methods: A literature search and review was conducted, regarding original research articles studying metabolic pathways of immune cells in high-grade gliomas. Searches were conducted in PubMed and Embase databases on May 15 and June 13, 2022. English-language original research articles were selected and prioritized based on their inclusion of findings related to metabolic changes in myeloid and lymphoid cells in the glioma tumor microenvironment.

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We measured brain injury markers, inflammatory mediators, and autoantibodies in 203 participants with COVID-19; 111 provided acute sera (1-11 days post admission) and 56 with COVID-19-associated neurological diagnoses provided subacute/convalescent sera (6-76 weeks post-admission). Compared to 60 controls, brain injury biomarkers (Tau, GFAP, NfL, UCH-L1) were increased in acute sera, significantly more so for NfL and UCH-L1, in patients with altered consciousness. Tau and NfL remained elevated in convalescent sera, particularly following cerebrovascular and neuroinflammatory disorders. Acutely, inflammatory mediators (including IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) were higher in participants with altered consciousness, and correlated with brain injury biomarker levels. Inflammatory mediators were lower than acute levels in convalescent sera, but levels of CCL2, CCL7, IL-1RA, IL-2Rα, M-CSF, SCF, IL-16 and IL-18 in individual participants correlated with Tau levels even at this late time point. When compared to acute COVID-19 patients with a normal GCS, network analysis showed significantly altered immune responses in patients with acute alteration of consciousness, and in convalescent patients who had suffered an acute neurological complication. The frequency and range of autoantibodies did not associate with neurological disorders. However, autoantibodies against specific antigens were more frequent in patients with altered consciousness in the acute phase (including MYL7, UCH-L1, GRIN3B, and DDR2), and in patients with neurological complications in the convalescent phase (including MYL7, GNRHR, and HLA antigens). In a novel low-inoculum mouse model of SARS-CoV-2, while viral replication was only consistently seen in mouse lungs, inflammatory responses were seen in both brain and lungs, with significant increases in CCL4, IFNγ, IL-17A, and microglial reactivity in the brain. Neurological injury is common in the acute phase and persists late after COVID-19, and may be driven by a para-infectious process involving a dysregulated host response.