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Recent reports have highlighted rare, and sometimes fatal, cases of cerebral venous sinus thrombosis (CVST) and thrombocytopenia following the Vaxzevria vaccine. An underlying immunological mechanism similar to that of spontaneous heparin-induced thrombocytopenia (HIT) is suspected, with the identification of antibodies to platelet factor-4 (PF4), but without previous heparin exposure. This unusual mechanism has significant implications for the management approach used, which differs from usual treatment of CVST. We describe the cases of two young males, who developed severe thrombocytopenia and fatal CVST following the first dose of Vaxzevria. Both presented with a headache, with subsequent rapid neurological deterioration. One patient underwent PF4 antibody testing, which was positive. A rapid vaccination programme is essential in helping to control the COVID-19 pandemic. Hence, it is vital that such COVID-19 vaccine-associated events, which at this stage appear to be very rare, are viewed through this lens. However, some cases have proved fatal. It is critical that clinicians are alerted to the emergence of such events to facilitate appropriate management. Patients presenting with CVST features and thrombocytopenia post-vaccination should undergo PF4 antibody testing and be managed in a similar fashion to HIT, in particular avoiding heparin and platelet transfusions.

Autism spectrum disorder (ASD) is characterised by the concomitant occurrence of impaired social interaction; restricted, perseverative and stereotypical behaviour; and abnormal communication skills. Recent epidemiological studies have reported a dramatic increase in the prevalence of ASD with as many as 1 in every 59 children being diagnosed with ASD. The fact that ASD appears to be principally genetically driven, and may be reversible postnatally, has raised the exciting possibility of using gene therapy as a disease-modifying treatment. Such therapies have already started to seriously impact on human disease and particularly monogenic disorders (e.g. metachromatic leukodystrophy, SMA type 1). In regard to ASD, technical advances in both our capacity to model the disorder in animals and also our ability to deliver genes to the central nervous system (CNS) have led to the first preclinical studies in monogenic ASD, involving both gene replacement and silencing. Furthermore, our increasing awareness and understanding of common dysregulated pathways in ASD have broadened gene therapy’s potential scope to include various polygenic ASDs. As this review highlights, despite a number of outstanding challenges, gene therapy has excellent potential to address cognitive dysfunction in ASD.

Objectives The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. Methods Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports (‘reference-standard report labels’); a subset of these examinations (n = 250) were assigned ‘reference-standard image labels’ by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. Results Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. Conclusions Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. Key Points • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

Background Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. Methods We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. Results We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67–82]), than encephalopathy (54% [42–65]). Intensive care use was high (38% [35–41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27–32]. The hazard of death was comparatively lower for patients in the WHO European region. Interpretation Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.

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BACKGROUND AND PURPOSE: Diagnosis of coronavirus disease 2019 (COVID-19) relies on clinical features and reverse-transcriptase polymerase chain reaction testing, but the sensitivity is limited. Carotid CTA is a routine acute stroke investigation and includes the lung apices. We evaluated CTA as a potential COVID-19 diagnostic imaging biomarker. MATERIALS AND METHODS: This was a multicenter, retrospective study (n ¼ 225) including CTAs of patients with suspected acute stroke from 3 hyperacute stroke units (March-April 2020). We evaluated the reliability and accuracy of candidate diagnostic imaging biomarkers. Demographics, clinical features, and risk factors for COVID-19 and stroke were analyzed using univariate and multivariate statistics. RESULTS: Apical ground-glass opacification was present in 22.2% (50/225) of patients. Ground-glass opacification had high interrater reliability (Fleiss k ¼ 0.81; 95% CI, 0.68-0.95) and, compared with reverse-transcriptase polymerase chain reaction, had good diagnostic performance (sensitivity, 75% [95% CI, 56-87]; specificity, 81% [95% CI, 71-88]; OR ¼ 11.65 [95% CI, 4.14-32.78]; P , .001) on multivariate analysis. In contrast, all other contemporaneous demographic, clinical, and imaging features available at CTA were not diagnostic for COVID-19. The presence of apical ground-glass opacification was an independent predictor of increased 30-day mortality (18.0% versus 5.7%, P ¼ .017; hazard ratio ¼ 3.51; 95% CI, 1.42-8.66; P ¼ .006). CONCLUSIONS: We identified a simple, reliable, and accurate COVID-19 diagnostic and prognostic imaging biomarker obtained from CTA lung apices: the presence or absence of ground-glass opacification. Our findings have important implications in the management of patients presenting with suspected stroke through early identification of COVID-19 and the subsequent limitation of disease transmission. ABBREVIATIONS: BSTI ¼ British Society of Thoracic Imaging; COVID-19 ¼ coronavirus disease 2019; GGO ¼ ground-glass opacification; IRR ¼ interrater reliability; RT-PCR ¼ reverse-transcriptase polymerase chain reaction; SARS-CoV-2 ¼ Severe Acute Respiratory Syndrome coronavirus 2 T he Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) was given pandemic status by the World Health Organization in March 2020. 1,2 When symptomatic, coronavirus disease 2019 (COVID-19) typically causes mild, self-limiting respiratory features. However, a severe lower respiratory and multisystem disease may occur, necessitating hospitalization. 3 Approximately 6.0% of patients with COVID-19 die, and 12% require intensive care support. 4-7 Symptoms alone are insufficient for a diagnosis due to a high prevalence of asymptomatic carriers and a variable presymptomatic incubation period (2-14 days). 8,9 The diagnostic reference standard is the reverse-transcriptase polymerase chain reaction

Mutations in the PARK2 gene have been implicated in the pathogenesis of early-onset Parkinson's disease. We present a case of movement disorder in a 4-year-old child from consanguineous parents and with a family history of Dopamine responsive dystonia, who was diagnosed with early-onset Parkinson's disease based on initial identification of a pathogenic PARK2 mutation. However, the evolution of the child's clinical picture was unusually rapid, with a preponderance of pyramidal rather than extrapyramidal symptoms, leading to re-investigation of the case with further imaging and genetic sequencing. Interestingly, a second homozygous mutation in the FA2H gene, implicated in Hereditary spastic paraplegia, was revealed, appearing to have contributed to the novel phenotype observed, and highlighting a potential interaction between the two mutated genes.