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Objective: This paper presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question answering systems., Materials and Methods: The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery., Results: The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions., Discussion: BioASQ Synergy aspire to provide a tool that gives the experts easy and personalised access to the latest findings in a fast growing corpus of material., Conclusion: In this paper we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the 'Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics' to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases., Competing Interests: Declaration of interests: None declared.

Highlights: Trunk frontal plane kinematics is the most sensitive parameter to fatigue. Practitioners should consider this finding during endurance training.Kinetics exhibit a stable linear increase in mean values but a non-linear increase in variance during an exhaustive incremental treadmill run. This may affect training at a submaximal fatigued state.Specific areas in the joint distributions of kinetics and kinematics during treadmill running exhibit increased sensitivity in predicting fatigue state.

The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for genome editing. Current gRNA design tools serve as an important platform for the efficient application of the CRISPR systems. However, most of the existing tools are black-box models that suffer from limitations, such as variable performance and unclear mechanism of decision making. Here, we introduce CRISPRedict, an interpretable gRNA efficiency prediction model for CRISPR-Cas9 gene editing. Its strength lies in the fact that it can accurately predict efficient guide RNAs-with equivalent performance to state-of-the-art tools-while being a simple linear model. Implemented as a user-friendly web server, CRISPRedict offers (i) quick and accurate predictions across various experimental conditions (e.g. U6/T7 transcription); (ii) regression and classification models for scoring gRNAs and (iii) multiple visualizations to explain the obtained results. Given its performance, interpretability, and versatility, we expect that it will assist researchers in the gRNA design process and facilitate genome editing research. CRISPRedict is available for use at http://www.crispredict.org/., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR-Cas9 guide design., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that is contained in samples, giving unimaginable opportunities to the scientific community. Laboratories equipped with these instruments produce a lot of data daily that can be digitally archived. Digital storage of data opens up the opportunity for retrospective suspect screening investigations for the occurrence of chemicals in the stored chromatograms. The first step of this approach involves the prediction of which data is more appropriate to be searched. In this study, we built an optimized multi-label classifier for predicting the most appropriate instrumental method (LC-HRMS or GC-HRMS or both) for the analysis of chemicals in digital specimens. The approach involved the generation of a baseline model based on the knowledge that an expert would use and the generation of an optimized machine learning model. A multi-step feature selection approach, a model selection strategy, and optimization of the classifier's hyperparameters led to a model with accuracy that outperformed the baseline implementation. The models were used to predict the most appropriate instrumental technique for new substances. The scripts are available at GitHub and the dataset at Zenodo.

Mounting evidence indicates an association between adipokines and inflammation-related atherosclerosis. Here, we sought to investigate the association of vaspin and omentin with clinical characteristics and outcomes of patients with acute cerebral ischemia (ACI). Consecutive ACI patients were evaluated within 24 h from symptom-onset. Stroke aetiology was classified using TOAST criteria. Adipokines were assayed using quantikine enzyme immunoassay commercially available kits. Stroke severity was assessed by NIHSS-score, and ipsilateral carotid stenosis (≥50% by NASCET criteria) by ultrasound and CT/MR angiography. Major cerebrovascular events were assessed at three months. We included 135 ACI patients (05 (78%) and 30 (22%) with acute ischemic stroke and transient ischemic attack, respectively; mean age ± SD: 59 ± 10 years; 68% men; median NIHSS-score: 3 (IQR:1-7)). Omentin was strongly correlated to admission stroke severity (Spearman rho coefficient: +0.303; p < 0.001). Patients with ipsilateral carotid stenosis had higher omentin levels compared to patients without stenosis (13.3 ± 8.9 ng/mL vs. 9.5 ± 5.5 ng/mL, p = 0.014). Increasing omentin levels were independently associated with higher stroke severity (linear regression coefficient = 0.290; 95%CI: 0.063-0.516; p = 0.002) and ipsilateral carotid stenosis (linear regression coefficient = 3.411; 95%CI: 0.194-6.628; p = 0.038). No association of vaspin with clinical characteristics and outcomes was found. Circulating omentin may represent a biomarker for the presence of atherosclerotic plaque, associated with higher stroke severity in ACI patients.

Background and Purpose: In recent years, the use of coiling has gained increased popularity for the treatment of intracranial aneurysms, and stroke physicians are confronted with rare pathologies associated with this relatively new and evolving treatment method, such as embolization of pieces of the polymeric filaments from the coils and a subsequent inflammatory response. In particular, white matter enhancing lesions are a rare complication after aneurysm endovascular therapy (EVT), suggesting a foreign body reaction to shedding of hydrophilic coating from the endovascular devices into the blood stream. The description of such a case aims to raise the clinicians' awareness of the symptomatic delayed and recurring inflammatory changes that may occur after endovascular aneurysmal treatment with the use of coiling devices., Case Description: A 64-year-old woman underwent coiling of a ruptured right posterior communicating artery aneurysm. She was asymptomatic after EVT. One year later, she presented with headache, acoustic hallucinations, paresthesias and left arm weakness. Brain magnetic resonance imaging (MRI) revealed multiple enhancing white matter lesions in the right hemisphere. She was treated with pulse intravenous methylprednisolone, followed by oral prednisolone; all clinical symptoms resolved and imaging findings improved substantially. Two years after tapering the steroids, follow-up symptoms recurred and repeat brain MRI revealed new enhancing white matter lesions., Discussion and Conclusions: There is an increasing number of similar reports of enhancing white matter lesions after coiling of intracranial aneurysms, with the incidence estimated to be between 0.5% and 2.3% in different cohort studies. Close monitoring for the appearance of new neurologic symptoms that could suggest delayed brain reactivity should be recommended., (© 2021 European Academy of Neurology.)

The purpose of the present study was to investigate the rotational kinematics pattern of the upper trunk and the pelvis and the complexity-variability of their movement, during the sprint butterfly stroke between male and female swimmers with long fins (18-26 cm) and without fins. Two pairs of 3D accelerometers and gyroscopes were used to measure segments' flexion-extension angles. There were no gender differences apart from the record. The amplitude values at the pelvis were significantly larger when swimming without fins while at the C7 they were significantly larger when swimming with them. Autocorrelation coefficients of angles' time histories were higher when swimming with fins for both segments. The power spectrums revealed a dominant frequency representing the stroke period which grew significantly stronger for the fin condition. Correlation Dimension verified a lower dimensionality for the fin condition for the C7 segment movement pattern. Overall fin use seems to offer "strength" to the swimmer's movement pattern. This does not imply better movement coordination, but rather a trend toward a more stable attractor., (Copyright © 2016 Elsevier Ltd. All rights reserved.)