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.

Rationale: The COVID-19 cases increased very fast in January and February 2020. The mortality among critically ill patients, especially the elder ones, is relatively high. Considering many patients died of severe inflammation response, it is urgent to develop effective therapeutic strategies for these patients. The human umbilical cord mesenchymal stem cells (hUCMSCs) have shown good capabilities to modulate the immune response and repair the injured tissue. Therefore, investigating the potential of hUCMSCs to the treatment of COVID-19 critically ill patients is necessary. Patient concerns: A 65-year-old woman felt fatigued and had a fever with body temperature of 38.2 ° C, coughed up white foaming sputum. After 1 day, she had chest tightness with SPO 2 of 81%, and blood pressure of 160/91 mm Hg. Diagnose: According to the guideline for the diagnosis and treatment of 2019 novel coronavirus infected pneumonia (Trial 4th Edition), COVID-19 was diagnosed, based on the real-time RT-PCR test of SARS-CoV-2. Interventions: After regular treatment for 12 days, the inflammation symptom of the patient was still very severe and the potential side effects of corticosteroid were observed. Then, allogenic hUCMSCs were given 3 times (5 × 10 7 cells each time) with a 3-day interval, together with thymosin α1 and antibiotics daily injection. Outcomes: After these treatments, most of the laboratory indexes and CT images showed remission of the inflammation symptom. The patient was subsequently transferred out of ICU, and the throat swabs test reported negative 4 days later. Lessons: These results indicated the clinical outcome and good tolerance of allogenic hUCMSCs transfer.

Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years. The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible. Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved. First, this review starts with a brief introduction to the history of CNN. Second, we provide an overview of CNN. Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection. Fifth, the applications of onedimensional, two-dimensional, and multi-dimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.

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helping medical staff members and radiologists around the world improve their understanding of this disease.CT can play a vital role in the early detection and management of COVID-19 (1,2). However, it is worth emphasizing that a patient with reverse-transcription polymerase chain reaction (RT-PCR) confirmed COVID-19 infection may have normal chest CT findings at admission. Bernheim et al (3) reported 20 (56%) of 36 patients imaged 0-2 days after symptom onset had normal CT findings. Fang et al (4) reported one of 51 (2%) patient imaged 3±3 days after symptom onset with normal CT scans. Ai et al (5) reported 21 of 601 (3%) RT-PCR positive patients with clinical symptoms had normal CT scans. In contrast, Pan et al (6) reported four of 21 (19%) patients with first normal CT had lung abnormalities on the follow-up CT approximately 4 days later. In our experience (7), of 17 of 149 (11.4%) symptomatic patients with normal chest CT on admission, 12 remained negative 10 days later with 2-3 follow-up CT examinations; the chest CT scans of the other five patients became positive over an average of 7 days. These reports confirm that a normal chest CT scan cannot exclude the diagnosis of COVID-19, especially for patients with early onset of symptoms.This copy is for personal use only. To order printed copies, contact reprints@rsna.org

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Purpose To determine the correlation between diffusion kurtosis imaging (DKI)-derived parameters and prognostic factors for rectal adenocarcinoma. Materials and Methods This study was approved by the local institute review board, and written informed consent was obtained from each patient. Data from 56 patients (median age, 59.5 years; age range, 31-86 years) with rectal adenocarcinoma between April 2014 and September 2015 were involved in this prospective study. DKI (b = 0, 700, 1400, and 2100 sec/mm) and conventional diffusion-weighted imaging (b = 0, 1000 sec/mm) were performed. Kurtosis and diffusivity from DKI and apparent diffusion coefficients (ADCs) from diffusion-weighted imaging were measured by two radiologists. Student t test, receiver operating characteristic curves, and Spearman correlation were used for statistical analysis. Results Kurtosis was significantly higher in high-grade than in low-grade rectal adenocarcinomas on the basis of both the number of poorly differentiated clusters (PDCs) (1.136 ± 0.086 vs 0.988 ± 0.060, P < .05) and World Health Organization (WHO) grades (1.103 ± 0.086 [standard deviation] vs 1.034 ± 0.103, P < .05). In PDC grading, the diffusivity and ADC were significantly lower in high-grade tumors than in low-grade tumors (1.187 ± 0.150 vs 1.306 ± 0.129 and 1.020 ± 0.113 vs 1.108 ± 0.097, respectively; P < .05) and showed similar correlations with histologic grades (r = -0.486 and r = -0.406, respectively; P > .05). Compared with both diffusivity and ADC, kurtosis showed significantly higher sensitivity (83.3% [20 of 24] vs 70.8% [17 of 24] and 70.8% [17 of 24], respectively) and specificity (96.8% [31 of 32] vs 84.4% [24 of 32] and 81.3% [26 of 32], respectively). Kurtosis showed a better correlation with PDC grades than with WHO grades (r = 0.797 vs r = 0.293, P < .05). Kurtosis was significantly higher in pN1-2 than in pN0 tumors (1.086 ± 0.103 vs 1.009 ± 0.086, P < .05). Conclusion Kurtosis derived from DKI demonstrated a higher correlation with histologic grades compared with diffusivity and ADC. It also showed better performance in differentiating between high- and low-grade rectal adenocarcinomas and between pN1-2 and pN0 tumors. RSNA, 2016.