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Abstract Background This multi-center study aimed to identify factors affecting fever and delayed defervescence in bacterial meningitis (BM) patients under 3 years of age because of the variability of fever in this patient population. Methods Only BM patients under 3 years treated at 49 centers in China from November 2018 to end-April 2021 were included in the study. Univariate and multivariate logistic regression analyses were performed to determine factors associated with afebrile presentation and fever of delayed defervescence. Results A total of 863 BM patients under 3 years were included in the study. Coagulase negative staphylococcus was associated with afebrile presentation (OR = 1.176), while septicaemia and ear-nose-throat infections were associated with fever (P

Abstract Background Noonan syndrome is an autosomal dominant genetic disorder that can occur in men and women and has a sporadic or family history. NS can lead to abnormal bleeding, but cerebral haemorrhage is rare. This is the first case of cerebral haemorrhage with a RAF1 gene mutation that originated in the neonatal period. Case presentation This case presents a newborn with a RAF1 gene mutation resulting in NS complicated with an abnormality of chromosome 46, X, del (Y) (q12). In the course of treatment, the baby's breathing suddenly increased. After an MRI examination of the skull, haemorrhaging was found in multiple parts of the brain. Conclusions After symptomatic treatment, the baby recovered well, but the main cause of cerebral haemorrhage was not found.

Background: Resting myocardial perfusion (MP) and wall motion (WM) imaging during real-time myocardial contrast echocardiography (MCE) improves the detection of coronary artery disease (CAD). However, its prognostic role in different clinical settings (emergency department and outpatient setting) remains unclear. Methods: A systematic search in PubMed and Embase databases, and the Cochrane library, was conducted to evaluate the role of resting MP and WM in predicting major adverse cardiac events (MACE), including death, nonfatal myocardial infarction (NFMI) and urgent revascularization in patients presenting to either outpatient clinics or emergency departments with suspected symptomatic CAD. Summary receiver operating characteristic (SROC) curves, sensitivity and specificity plots were applied to assess diagnostic performance using RevMan 5.3. Results: Seven studies met criteria, including 3668 patients (six with follow up ranging from 2 days to 2.6 years). The Relative Risk (RR) for predicting MACE in patients with both abnormal resting MP and WM was 6.1 (95% CI, 5.1–7.2) and 14.3 (95% CI, 10.3–19.8) for death/NFMI, when compared to normal resting MP and WM patients. Having both abnormal resting MP and WM was also more predictive of MACE (RR, 1.7; 95% CI 1.5–1.9) and death/NFMI (RR, 2.2; 95% CI, 1.8–2.7) when compared to abnormal WM with normal resting MP. Conclusion: In this meta-analysis of both ED and outpatient clinic presentations for suspected CAD, having both a resting regional MP and WM abnormality identifies the highest risk patient for adverse events.

Abstract Background Electronic Medical Record (EMR) comprises patients’ medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data. Methods A multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task. Results The performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point. Conclusions In this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.