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Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care. [ABSTRACT FROM AUTHOR]

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors. Chronic lymphocytic leukemia is an indolent disease, and many patients succumb to infection rather than the direct effects of the disease. Here, the authors use medical records and machine learning to predict the patients that may be at risk of infection, which may enable a change in the course of their treatment. [ABSTRACT FROM AUTHOR]

The lifestyle of intracellular pathogens, such as malaria parasites, is intimately connected to that of their host, primarily for nutrient supply. Nutrients act not only as primary sources of energy but also as regulators of gene expression, metabolism and growth, through various signalling networks that enable cells to sense and adapt to varying environmental conditions. Canonical nutrient-sensing pathways are presumed to be absent from the causative agent of malaria, Plasmodium, thus raising the question of whether these parasites can sense and cope with fluctuations in host nutrient levels. Here we show that Plasmodium blood-stage parasites actively respond to host dietary calorie alterations through rearrangement of their transcriptome accompanied by substantial adjustment of their multiplication rate. A kinome analysis combined with chemical and genetic approaches identified KIN as a critical regulator that mediates sensing of nutrients and controls a transcriptional response to the host nutritional status. KIN shares homology with SNF1/AMPKα, and yeast complementation studies suggest that it is part of a functionally conserved cellular energy-sensing pathway. Overall, these findings reveal a key parasite nutrient-sensing mechanism that is critical for modulating parasite replication and virulence. [ABSTRACT FROM AUTHOR]

Genome manipulation in the malaria parasite Plasmodium falciparum remains largely intractable and improved genomic tools are needed to further understand pathogenesis and drug resistance. We demonstrated the CRISPR-Cas9 system for use in P. falciparum by disrupting chromosomal loci and generating marker-free, single-nucleotide substitutions with high efficiency. Additionally, an artemisinin-resistant strain was generated by introducing a previously implicated polymorphism, thus illustrating the value of efficient genome editing in malaria research. [ABSTRACT FROM AUTHOR]

The article discusses the disadvantages of using the online tool, CRISPR-Cas9 endonuclease system, for single guide RNA (sgRNA) which include limited flexibility and throughput, its availability for only few organisms, and the limited mismatches on the number of target-sgRNA. The flexibility and speed provided by offline solutions such as sgRNAcas9 is stated. A chart is presented depicting the offline software, Protospacer Workbench, for rapid, flexible design of Cas9 sgRNA.