Delayed Antibody Response in the Acute Phase of Infection Is Associated with a Lower Mental Component of Quality of Life in Survivors of Severe and Critical COVID-19.

Background: The long-term sequelae of coronavirus disease 2019 (COVID-19) significantly affects quality of life (QoL) in disease survivors. Delayed development of the adaptive immune response is associated with more severe disease and a worse prognosis in COVID-19. The effects of delayed immune response on COVID-19 sequelae and QoL are unknown. Methods: We conducted a prospective study to assess the relationship between the delayed antibody response in the acute phase of infection in naïve unvaccinated patients suffering from severe or critical COVID-19 and their QoL 12 months after hospital discharge. The 12-item Short Form Survey (SF-12) questionnaire was used for assessment of QoL. The SF-12 evaluates both mental and physical components of QoL, incorporating a mental component score (MCS-12) and a physical component score (PCS-12). A delayed antibody response was defined as testing negative for anti-spike SARS-CoV-2 antibodies at the time of hospital admission. Results: The study included 274 patients (154 men and 120 women). Of the enrolled patients, 144 had a delayed immune response. These patients had a significantly lower MCS-12 (p = 0.002), but PCS-12 (p = 0.397) was not significantly different at the 12-month follow-up compared to patients with positive anti-spike SARS-CoV-2 antibodies. The MCS-12 at the time of follow-up was negatively associated with delayed antibody response irrespective of possible confounders (p = 0.006; B = 3.609; ηp2 = 0.035; 95% CI = 1.069-6.150). An MSC-12 below 50 points at the time of follow-up was positively associated with delayed antibody response (p = 0.001; B = 1.092; OR = 2.979; 95% CI = 1.554-5.711). Conclusions: This study confirmed that, in patients with severe and critical COVID-19, a negative result for anti-spike SARS-CoV-2 antibodies at the time of hospital admission is associated with a lower mental component of QoL in unvaccinated patients naïve to COVID-19 one year after hospital discharge.

International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.

Aims: A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results: An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion: The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.

Validation of an automated artificial intelligence system for 12­lead ECG interpretation.

BACKGROUND: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE. METHODS: An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12­lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: A total of 932,711 standard 12­lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses. CONCLUSIONS: Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12­lead ECG, highlighting its potential as a clinical tool for healthcare professionals.

Anti-SARS-CoV-2 Antibody Status at the Time of Hospital Admission and the Prognosis of Patients with COVID-19: A Prospective Observational Study.

The association between COVID-19 severity and antibody response has not been clearly determined. We aimed to assess the effects of antibody response to SARS-CoV-2 S protein at the time of hospital admission on in-hospital and longitudinal survival. Methods: A prospective observational study in naive hospitalised COVID-19 patients. The presence of anti-S SARS-CoV-2 IgM and IgG was evaluated using a lateral flow assay at the time of admission. The patients were followed up for 8-30 months to assess survival. We recruited 554 patients (330 men and 224 women). Overall, 63.0% of the patients had positive IgG or IgM anti-S SARS-CoV-2 antibodies at the time of hospital admission. In the univariate analysis, the patients with negative anti-S SARS-CoV-2 IgM and IgG antibodies were referred to the hospital sooner, had lower CRP and D-dimer concentrations, and were hospitalised longer. They were also more likely to be admitted to an intensive care unit and more often received baricitinib treatment. During their hospital stay, 8.5% of the antibody-positive and 22.3% of the antibody-negative patients died (p = 0.0001). The median duration of the follow-up was 21 months. During the follow-up after hospital discharge, 3.6% of antibody-positive and 9.1% of antibody-negative patients died (p = 0.027). In the multivariate analysis, the negative anti-S SARS-CoV-2 antibodies were associated with a higher risk of in-hospital death (OR 3.800; 95% CI 1.844-7.829; p = 0.0001) and with a higher risk of death during follow-up (OR 2.863; 95% CI 1.110-7.386; p = 0.030). These associations were independent of age, the time from symptom onset to hospital admission, CRP, D-Dimer, the number of comorbidities, disease severity at the time of hospital admission, and baricitinib therapy. Our study concludes that negative anti-S SARS-CoV-2 IgM and IgG at the time of admission are associated with higher in-hospital mortality and cause a higher risk of all-cause death during follow-up after discharge.

Dynamic Pooling Improves Nanopore Base Calling Accuracy.

In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.

Nanopore base calling on the edge.

MOTIVATION: MinION is a portable nanopore sequencing device that can be easily operated in the field with features including monitoring of run progress and selective sequencing. To fully exploit these features, real-time base calling is required. Up to date, this has only been achieved at the cost of high computing requirements that pose limitations in terms of hardware availability in common laptops and energy consumption. RESULTS: We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed new versions of two key components used in convolutional neural networks for speech recognition and base calling. In our components, we propose a new way of factorization of a full convolution into smaller operations, which decreases memory access operations, memory access being a bottleneck on this device. DeepNano-coral achieves real-time base calling during sequencing with the accuracy slightly better than the fast mode of the Guppy base caller and is extremely energy efficient, using only 10 W of power. AVAILABILITY AND IMPLEMENTATION: https://github.com/fmfi-compbio/coral-basecaller. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepNano-blitz: a fast base caller for MinION nanopore sequencers.

MOTIVATION: Oxford Nanopore MinION is a portable DNA sequencer that is marketed as a device that can be deployed anywhere. Current base callers, however, require a powerful GPU to analyze data produced by MinION in real time, which hampers field applications. RESULTS: We have developed a fast base caller DeepNano-blitz that can analyze stream from up to two MinION runs in real time using a common laptop CPU (i7-7700HQ), with no GPU requirements. The base caller settings allow trading accuracy for speed and the results can be used for real time run monitoring (i.e. sample composition, barcode balance, species identification, etc.) or prefiltering of results for more detailed analysis (i.e. filtering out human DNA from human-pathogen runs). AVAILABILITY AND IMPLEMENTATION: DeepNano-blitz has been developed and tested on Linux and Intel processors and is available under MIT license at https://github.com/fmfi-compbio/deepnano-blitz. CONTACT: vladimir.boza@fmph.uniba.sk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Continuous Sound Collection Using Smartphones and Machine Learning to Measure Cough.

BACKGROUND: Despite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need. OBJECTIVES: In light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection). METHODS: This work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count. RESULTS: Sound collection: the application demonstrated the ability to continuously record sounds using the phone's internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data. CONCLUSIONS: Sound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.

DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads.

The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the accuracy of base calling is much dependent on the underlying software and can be improved by considering modern machine learning methods. By employing carefully crafted recurrent neural networks, our tool significantly improves base calling accuracy on data from R7.3 version of the platform compared to the default base caller supplied by the manufacturer. On R9 version, we achieve results comparable to Nanonet base caller provided by Oxford Nanopore. Availability of an open source tool with high base calling accuracy will be useful for development of new applications of the MinION device, including infectious disease detection and custom target enrichment during sequencing.

GAML: genome assembly by maximum likelihood.

BACKGROUND: Resolution of repeats and scaffolding of shorter contigs are critical parts of genome assembly. Modern assemblers usually perform such steps by heuristics, often tailored to a particular technology for producing paired or long reads. RESULTS: We propose a new framework that allows systematic combination of diverse sequencing datasets into a single assembly. We achieve this by searching for an assembly with the maximum likelihood in a probabilistic model capturing error rate, insert lengths, and other characteristics of the sequencing technology used to produce each dataset. We have implemented a prototype genome assembler GAML that can use any combination of insert sizes with Illumina or 454 reads, as well as PacBio reads. Our experiments show that we can assemble short genomes with N50 sizes and error rates comparable to ALLPATHS-LG or Cerulean. While ALLPATHS-LG and Cerulean require each a specific combination of datasets, GAML works on any combination. CONCLUSIONS: We have introduced a new probabilistic approach to genome assembly and demonstrated that this approach can lead to superior results when used to combine diverse set of datasets from different sequencing technologies. Data and software is available at http://compbio.fmph.uniba.sk/gaml.