Introduction Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19). Methods EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I 2 , T 2 , and Cochrane Q, sensitivity analyses, and assessed publication bias. Results 148 studies met the inclusion criteria for the systematic review and meta-analysis with 12′149 patients (5′739 female) and a median age of 47.0 [35.0–64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78–1.72]; p < 0.001), being male (RR = 1.32 [1.13–1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48–1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially ( I 2 ; range: 1.5–98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05). Conclusions Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.

Weis (1) (2)*, Aline Cuénod (3) (4), Bastian Rieck (1) (2), Felipe Llinares-López (1) (2), Olivier Dubuis (8), Susanne Graf (7), Claudia Lang (8), Michael Oberle (9), Maximilian Brackmann (10), Kirstine K. Søgaard (3) (4), Michael Osthoff (5) (6), Karsten Borgwardt (1) (2)*+, Adrian Egli (3) (4)*+

Background: The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination. Objectives: The aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra. Data sources: Using PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification. Study eligibility criteria: Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded. Methods: A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted. Results: From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets. Conclusions: A growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.

Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care. Availability and implementation We make our code publicly available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE.

SUMMARY The physical organization of DNA enzymes at a replication fork enables efficient copying of two antiparallel DNA strands, yet dynamic protein interactions within the replication complex complicate replisome structural studies. We employed a combination of crystallographic, native mass spectrometry, and small-angle x-ray scattering experiments to capture alternative structures of a model replication system encoded by bacteriophage T7. Two molecules of DNA polymerase bind the ring-shaped primase-helicase in a conserved orientation and provide structural insight into how the acidic C-terminal tail of the primase-helicase contacts the DNA polymerase to facilitate loading of the polymerase onto DNA. A third DNA polymerase binds the ring in an offset manner that may enable polymerase exchange during replication. Alternative polymerase binding modes are also detected by small-angle x-ray scattering with DNA substrates present. Our collective results unveil complex motions within T7 replisome higher-order structures that are underpinned by multivalent protein-protein interactions with functional implications.

Early administration of effective antimicrobial treatments improves the outcome of infections. Culture-based antimicrobial resistance testing allows for tailored treatments, but takes up to 96h. We present a revolutionary approach to predict resistance with unmatched speed within 24h, using calibrated logistic regression and LightGBM-classifiers trained on species-specific MALDI-TOF mass spectrometry measurements. For this analysis, we created an unprecedented large, publicly-available dataset combining mass spectra and resistance information. Our models provide highly valuable treatment guidance 12–72h earlier than classical approaches. Rejection of uncertain predictions enables quality control and clinically-applicable sensitivities and specificities for the priority pathogens Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae.

Introduction Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical, societal, and economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19). Methods EMBASE, PubMed/ Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I2, Tau2, and Cochrane Q, sensitivity analyses, and assessed publication bias. Results: 148 met the inclusion criteria for the systematic review and meta-analysis with 12149 patients (5739 female) and a median age was 47.0 [35.0-64.6]. 617 patients died from COVID-19 and its complication, while 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78- 1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I2; range: 1.5-98.2%). Publication bias was only found in eight studies (Eggers test: p < 0.05). Conclusions: Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.