Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

BACKGROUND: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain. METHODS: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse event. RESULTS: A total of 2404 patients (mean age, 70 years) in China underwent randomization and provided consent for the trial: 1205 in the intervention group and 1199 in the usual-care group. The median time between symptom onset and randomization was 61 minutes (interquartile range, 41 to 93), and the mean blood pressure at randomization was 178/98 mm Hg. Stroke was subsequently confirmed by imaging in 2240 patients, of whom 1041 (46.5%) had a hemorrhagic stroke. At the time of patients' arrival at the hospital, the mean systolic blood pressure in the intervention group was 159 mm Hg, as compared with 170 mm Hg in the usual-care group. Overall, there was no difference in functional outcome between the two groups (common odds ratio, 1.00; 95% confidence interval [CI], 0.87 to 1.15), and the incidence of serious adverse events was similar in the two groups. Prehospital reduction of blood pressure was associated with a decrease in the odds of a poor functional outcome among patients with hemorrhagic stroke (common odds ratio, 0.75; 95% CI, 0.60 to 0.92) but an increase among patients with cerebral ischemia (common odds ratio, 1.30; 95% CI, 1.06 to 1.60). CONCLUSIONS: In this trial, prehospital blood-pressure reduction did not improve functional outcomes in a cohort of patients with undifferentiated acute stroke, of whom 46.5% subsequently received a diagnosis of hemorrhagic stroke. (Funded by the National Health and Medical Research Council of Australia and others; INTERACT4 ClinicalTrials.gov number, NCT03790800; Chinese Trial Registry number, ChiCTR1900020534.).

DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.

BACKGROUND: MiRNAs are involved in the occurrence and development of many diseases. Extensive literature studies have demonstrated that miRNA-disease associations are stratified and encompass ~ 20% causal associations. Computational models that predict causal miRNA-disease associations provide effective guidance in identifying novel interpretations of disease mechanisms and potential therapeutic targets. Although several predictive models for miRNA-disease associations exist, it is still challenging to discriminate causal miRNA-disease associations from non-causal ones. Hence, there is a pressing need to develop an efficient prediction model for causal miRNA-disease association prediction. RESULTS: We developed DNI-MDCAP, an improved computational model that incorporated additional miRNA similarity metrics, deep graph embedding learning-based network imputation and semi-supervised learning framework. Through extensive predictive performance evaluation, including tenfold cross-validation and independent test, DNI-MDCAP showed excellent performance in identifying causal miRNA-disease associations, achieving an area under the receiver operating characteristic curve (AUROC) of 0.896 and 0.889, respectively. Regarding the challenge of discriminating causal miRNA-disease associations from non-causal ones, DNI-MDCAP exhibited superior predictive performance compared to existing models MDCAP and LE-MDCAP, reaching an AUROC of 0.870. Wilcoxon test also indicated significantly higher prediction scores for causal associations than for non-causal ones. Finally, the potential causal miRNA-disease associations predicted by DNI-MDCAP, exemplified by diabetic nephropathies and hsa-miR-193a, have been validated by recently published literature, further supporting the reliability of the prediction model. CONCLUSIONS: DNI-MDCAP is a dedicated tool to specifically distinguish causal miRNA-disease associations with substantially improved accuracy. DNI-MDCAP is freely accessible at http://www.rnanut.net/DNIMDCAP/ .

The third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3): an international, stepped wedge cluster randomised controlled trial.

BACKGROUND: Early control of elevated blood pressure is the most promising treatment for acute intracerebral haemorrhage. We aimed to establish whether implementing a goal-directed care bundle incorporating protocols for early intensive blood pressure lowering and management algorithms for hyperglycaemia, pyrexia, and abnormal anticoagulation, implemented in a hospital setting, could improve outcomes for patients with acute spontaneous intracerebral haemorrhage. METHODS: We performed a pragmatic, international, multicentre, blinded endpoint, stepped wedge cluster randomised controlled trial at hospitals in nine low-income and middle-income countries (Brazil, China, India, Mexico, Nigeria, Pakistan, Peru, Sri Lanka, and Viet Nam) and one high-income country (Chile). Hospitals were eligible if they had no or inconsistent relevant, disease-specific protocols, and were willing to implement the care bundle to consecutive patients (aged ≥18 years) with imaging-confirmed spontaneous intracerebral haemorrhage presenting within 6 h of the onset of symptoms, had a local champion, and could provide the required study data. Hospitals were centrally randomly allocated using permuted blocks to three sequences of implementation, stratified by country and the projected number of patients to be recruited over the 12 months of the study period. These sequences had four periods that dictated the order in which the hospitals were to switch from the control usual care procedure to the intervention implementation of the care bundle procedure to different clusters of patients in a stepped manner. To avoid contamination, details of the intervention, sequence, and allocation periods were concealed from sites until they had completed the usual care control periods. The care bundle protocol included the early intensive lowering of systolic blood pressure (target <140 mm Hg), strict glucose control (target 6·1-7·8 mmol/L in those without diabetes and 7·8-10·0 mmol/L in those with diabetes), antipyrexia treatment (target body temperature ≤37·5°C), and rapid reversal of warfarin-related anticoagulation (target international normalised ratio <1·5) within 1 h of treatment, in patients where these variables were abnormal. Analyses were performed according to a modified intention-to-treat population with available outcome data (ie, excluding sites that withdrew during the study). The primary outcome was functional recovery, measured with the modified Rankin scale (mRS; range 0 [no symptoms] to 6 [death]) at 6 months by masked research staff, analysed using proportional ordinal logistic regression to assess the distribution in scores on the mRS, with adjustments for cluster (hospital site), group assignment of cluster per period, and time (6-month periods from Dec 12, 2017). This trial is registered at Clinicaltrials.gov (NCT03209258) and the Chinese Clinical Trial Registry (ChiCTR-IOC-17011787) and is completed. FINDINGS: Between May 27, 2017, and July 8, 2021, 206 hospitals were assessed for eligibility, of which 144 hospitals in ten countries agreed to join and were randomly assigned in the trial, but 22 hospitals withdrew before starting to enrol patients and another hospital was withdrawn and their data on enrolled patients was deleted because regulatory approval was not obtained. Between Dec 12, 2017, and Dec 31, 2021, 10 857 patients were screened but 3821 were excluded. Overall, the modified intention-to-treat population included 7036 patients enrolled at 121 hospitals, with 3221 assigned to the care bundle group and 3815 to the usual care group, with primary outcome data available in 2892 patients in the care bundle group and 3363 patients in the usual care group. The likelihood of a poor functional outcome was lower in the care bundle group (common odds ratio 0·86; 95% CI 0·76-0·97; p=0·015). The favourable shift in mRS scores in the care bundle group was generally consistent across a range of sensitivity analyses that included additional adjustments for country and patient variables (0·84; 0·73-0·97; p=0·017), and with different approaches to the use of multiple imputations for missing data. Patients in the care bundle group had fewer serious adverse events than those in the usual care group (16·0% vs 20·1%; p=0·0098). INTERPRETATION: Implementation of a care bundle protocol for intensive blood pressure lowering and other management algorithms for physiological control within several hours of the onset of symptoms resulted in improved functional outcome for patients with acute intracerebral haemorrhage. Hospitals should incorporate this approach into clinical practice as part of active management for this serious condition. FUNDING: Joint Global Health Trials scheme from the Department of Health and Social Care, the Foreign, Commonwealth & Development Office, and the Medical Research Council and Wellcome Trust; West China Hospital; the National Health and Medical Research Council of Australia; Sichuan Credit Pharmaceutic and Takeda China.

m6Adecom: Analysis of m6A profile matrix based on graph regularized non-negative matrix factorization.

Epitranscriptomic m6A methylation is shown to mediate extensive regulations under the context of various RNA binding protein (RBP) readers. With m6A methylation data has reached a sizable scale, the functional context-aware analysis of m6A profiles is becoming more feasible and demanded. In this study, we employed graph regularized non-negative matrix factorization (GNMF) for m6A profile analysis and comparison, where the RBP binding preference of m6A sites were incorporated as the functional context-based graph constraint term. Compared to the baseline non-negative matrix factorization (NMF) method, this GNMF-based method could better capture the distinctions in multiple functional characteristics between different group of m6A sites, including but not limited to the associated biological pathways and disease genes. We further established m6Adecom, an online tool that can be used for correlation and enrichment analysis of m6A profiles using the matrix decomposition result from GNMF, and gene set enrichment analysis based on the high-score m6A sites. m6Adecom is freely accessible at http://www.rnanut.net/m6adecom.

De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models.

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from Australian hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: (1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; (2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and (3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine (SVM) method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six base-models, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods. The end-to-end framework provides a robust solution to de-identifying clinical narrative corpuses safely. It can easily be applied to any kind of clinical narrative documents.

Hierarchical label-wise attention transformer model for explainable ICD coding.

International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT + ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.

LnCompare: gene set feature analysis for human long non-coding RNAs.

Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets.

LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations.

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA-disease associations, but few models are available to further prioritize causal miRNA-disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA-disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA-disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA-disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA-disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA-disease associations from general miRNA-disease associations.

A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

High-Performance Motion Estimation for Image Sensors with Video Compression.

It is important to reduce the time cost of video compression for image sensors in video sensor network. Motion estimation (ME) is the most time-consuming part in video compression. Previous work on ME exploited intra-frame data reuse in a reference frame to improve the time efficiency but neglected inter-frame data reuse. We propose a novel inter-frame data reuse scheme which can exploit both intra-frame and inter-frame data reuse for ME in video compression (VC-ME). Pixels of reconstructed frames are kept on-chip until they are used by the next current frame to avoid off-chip memory access. On-chip buffers with smart schedules of data access are designed to perform the new data reuse scheme. Three levels of the proposed inter-frame data reuse scheme are presented and analyzed. They give different choices with tradeoff between off-chip bandwidth requirement and on-chip memory size. All three levels have better data reuse efficiency than their intra-frame counterparts, so off-chip memory traffic is reduced effectively. Comparing the new inter-frame data reuse scheme with the traditional intra-frame data reuse scheme, the memory traffic can be reduced by 50% for VC-ME.

A Novel 2D-to-3D Video Conversion Method Using Time-Coherent Depth Maps.

In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

A multi-modal face recognition method using complete local derivative patterns and depth maps.

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features.

Automated ICD coding using extreme multi-label long text transformer-based models.

Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. In this study, we investigated two existing Transformer-based models (PLM-ICD and XR-Transformer) and proposed a novel Transformer-based model (XR-LAT), aiming to address the extreme label set and long text classification challenges that are posed by automated ICD coding tasks. The Transformer-based model PLM-ICD, which currently holds the state-of-the-art (SOTA) performance on the ICD coding benchmark datasets MIMIC-III and MIMIC-II, was selected as our baseline model for further optimisation on both datasets. In addition, we extended the capabilities of the leading model in the general extreme multi-label text classification domain, XR-Transformer, to support longer sequences and trained it on both datasets. Moreover, we proposed a novel model, XR-LAT, which was also trained on both datasets. XR-LAT is a recursively trained model chain on a predefined hierarchical code tree with label-wise attention, knowledge transferring and dynamic negative sampling mechanisms. Our optimised PLM-ICD models, which were trained with longer total and chunk sequence lengths, significantly outperformed the current SOTA PLM-ICD models, and achieved the highest micro-F1 scores of 60.8 % and 50.9 % on MIMIC-III and MIMIC-II, respectively. The XR-Transformer model, although SOTA in the general domain, did not perform well across all metrics. The best XR-LAT based models obtained results that were competitive with the current SOTA PLM-ICD models, including improving the macro-AUC by 2.1 % and 5.1 % on MIMIC-III and MIMIC-II, respectively. Our optimised PLM-ICD models are the new SOTA models for automated ICD coding on both datasets, while our novel XR-LAT models perform competitively with the previous SOTA PLM-ICD models.

Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study.

BACKGROUND: The narrative free-text data in electronic medical records (EMRs) contain valuable clinical information for analysis and research to inform better patient care. However, the release of free text for secondary use is hindered by concerns surrounding personally identifiable information (PII), as protecting individuals' privacy is paramount. Therefore, it is necessary to deidentify free text to remove PII. Manual deidentification is a time-consuming and labor-intensive process. Numerous automated deidentification approaches and systems have been attempted to overcome this challenge over the past decade. OBJECTIVE: We sought to develop an accurate, web-based system deidentifying free text (DEFT), which can be readily and easily adopted in real-world settings for deidentification of free text in EMRs. The system has several key features including a simple and task-focused web user interface, customized PII types, use of a state-of-the-art deep learning model for tagging PII from free text, preannotation by an interactive learning loop, rapid manual annotation with autosave, support for project management and team collaboration, user access control, and central data storage. METHODS: DEFT comprises frontend and backend modules and communicates with central data storage through a filesystem path access. The frontend web user interface provides end users with a user-friendly workspace for managing and annotating free text. The backend module processes the requests from the frontend and performs relevant persistence operations. DEFT manages the deidentification workflow as a project, which can contain one or more data sets. Customized PII types and user access control can also be configured. The deep learning model is based on a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) with RoBERTa as the word embedding layer. The interactive learning loop is further integrated into DEFT to speed up the deidentification process and increase its performance over time. RESULTS: DEFT has many advantages over existing deidentification systems in terms of its support for project management, user access control, data management, and an interactive learning process. Experimental results from DEFT on the 2014 i2b2 data set obtained the highest performance compared to 5 benchmark models in terms of microaverage strict entity-level recall and F1-scores of 0.9563 and 0.9627, respectively. In a real-world use case of deidentifying clinical notes, extracted from 1 referral hospital in Sydney, New South Wales, Australia, DEFT achieved a high microaverage strict entity-level F1-score of 0.9507 on a corpus of 600 annotated clinical notes. Moreover, the manual annotation process with preannotation demonstrated a 43% increase in work efficiency compared to the process without preannotation. CONCLUSIONS: DEFT is designed for health domain researchers and data custodians to easily deidentify free text in EMRs. DEFT supports an interactive learning loop and end users with minimal technical knowledge can perform the deidentification work with only a shallow learning curve.

Effects of intensive blood pressure lowering on cerebral ischaemia in thrombolysed patients: insights from the ENCHANTED trial.

Background: Intensive blood pressure lowering may adversely affect evolving cerebral ischaemia. We aimed to determine whether intensive blood pressure lowering altered the size of cerebral infarction in the 2196 patients who participated in the Enhanced Control of Hypertension and Thrombolysis Stroke Study, an international randomised controlled trial of intensive (systolic target 130-140 mm Hg within 1 h; maintained for 72 h) or guideline-recommended (systolic target <180 mm Hg) blood pressure management in patients with hypertension (systolic blood pressure >150 mm Hg) after thrombolysis treatment for acute ischaemic stroke between March 3, 2012 and April 30, 2018. Methods: All available brain imaging were analysed centrally by expert readers. Log-linear regression was used to determine the effects of intensive blood pressure lowering on the size of cerebral infarction, with adjustment for potential confounders. The primary analysis pertained to follow-up computerised tomography (CT) scans done between 24 and 36 h. Sensitivity analysis were undertaken in patients with only a follow-up magnetic resonance imaging (MRI) and either MRI or CT at 24-36 h, and in patients with any brain imaging done at any time during follow-up. This trial is registered with ClinicalTrials.gov, number NCT01422616. Findings: There were 1477 (67.3%) patients (mean age 67.7 [12.1] y; male 60%, Asian 65%) with available follow-up brain imaging for analysis, including 635 patients with a CT done at 24-36 h. Mean achieved systolic blood pressures over 1-24 h were 141 mm Hg and 149 mm Hg in the intensive group and guideline group, respectively. There was no effect of intensive blood pressure lowering on the median size (ml) of cerebral infarction on follow-up CT at 24-36 h (0.3 [IQR 0.0-16.6] in the intensive group and 0.9 [0.0-12.5] in the guideline group; log Δmean -0.17, 95% CI -0.78 to 0.43). The results were consistent in sensitivity and subgroup analyses. Interpretation: Intensive blood pressure lowering treatment to a systolic target <140 mm Hg within several hours after the onset of symptoms may not increase the size of cerebral infarction in patients who receive thrombolysis treatment for acute ischaemic stroke of mild to moderate neurological severity. Funding: National Health and Medical Research Council of Australia; UK Stroke Association; UK Dementia Research Institute; Ministry of Health and the National Council for Scientific and Technological Development of Brazil; Ministry for Health, Welfare, and Family Affairs of South Korea; Takeda.

Benchmark of computational methods for predicting microRNA-disease associations.

BACKGROUND: A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. RESULTS: Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. CONCLUSION: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.