Background: Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, while several cohorts from China have been described, our understanding is limited, with no data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. To improve our understanding of the clinical characteristics of this emerging infection we carried out a retrospective review of patient data to examine the clinical characteristics of patients admitted for COVID-19 hospital treatment. Methods: Demographic, clinical and laboratory data on the rst 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records. Missing data were excluded from the analysis. Results: Fifty-eight per cent were male, 63% were Irish nationals, 29% were GMS eligible, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of ve days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal ndings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modi ed) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit-14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11). Conclusion: Our ndings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.

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HighlightsTo date, there is no standard MR imaging protocol to characterize the nigrosome1 territory of the substantia nigra.Nigrosome 1 can be consistently visualized using true SWI with a resolution of at least 0.67 × 0.67 × 1.34 mm3.Loss of nigrosome 1 on true SWI can differentiate Parkinsonism from healthy controls.Not all 'Parkinson's disease patients show bilateral loss of nigrosome 1.

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Objectives To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). Methods From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/ or hilar lymphadenopathy were also evaluated. Results Multivariate logistic regression analysis showed that history of exposure (β = 3.095, odds ratio (OR) = 22.088), leukocyte count (β = − 1.495, OR = 0.224), number of segments with peripheral lesions (β = 1.604, OR = 1.604), and crazy-paving pattern (β = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0-1 point) − 1 × leukocyte count (0-2 points) + 1 × peripheral lesions (0-1 point) + 2 × crazypaving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). Conclusions Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. Key Points • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.

Objective: To quantify coronavirus diseases 2019 (COVID-19) pneumonia and to explore whether quantitative computer tomography (CT) could be used to assess severity on admission. Materials and methods: From January 17 to February 9, 2020, 38 hospitalized patients with COVID-19 pneumonia were consecutively enrolled in our hospitals. All clinical data and the chest CT on admission were retrospectively reviewed and analyzed. Firstly, a quantitative method based on multi-scale convolutional neural networks was used to assess the infected lung segments and this was compared with the semi-quantitative method. Secondly, the quantitative method was tested with laboratory results and the pneumonia severity index (PSI) by correlation analyses. Thirdly, both quantitative and semi-quantitative parameters between patients with different PSI were compared. Results: Thirty cases were finally enrolled: 16 (53.33%) of them were male, and the mean age was 48 years old. The interval from onset symptoms to first chest CT scan was 8 days. The proportion of ground glass opacity (GGO), consolidation and the total lesion based on the quantitative method was positively correlated with the semi-quantitative CT score (P < 0.001 for all; rs ¼ 0.88, 0.87, 0.90), CRP (P ¼ 0.0278, 0.0168, 0.0078; rs ¼ 0.40, 0.43, 0.48) and ESR (P ¼ 0.0296, 0.0408, 0.0048; rs ¼ 0.46, 0.44, 0.58), respectively, and was negatively correlated with the lymphocyte count (P ¼ 0.0222, 0.0024, 0.0068; rs ¼ À0.42, À0.53, À0.48). There was a positive correlation trend between the proportion of total infection and the pneumonia severity index (P ¼ 0.0994; rs ¼ 0.30) and a tendency that patients with severe COVID-19 pneumonia had higher percentage of consolidation and total infection (P ¼ 0.0903, 0.0989). Conclusions: Quantitative CT may have potential in assessing the severity of COVID-19 pneumonia on admission.