Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

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Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with increasing incidence. The externalizing and internalizing problems among children with ASD often persistent and highly impair functioning of both the child and the family. Children with ASD often develop gut-related comorbidities and dysbiosis can have negative effects on not only the gastrointestinal (GI) tract, but also psychological symptoms. Dietary exclusions and probiotic supplements also have been investigated in the management of ASD symptoms. Especially, there is some anecdotal evidence that probiotics supplements are able to alleviate GI symptoms as well as improve behaviors in children with ASD. Method and analysis: This review will report on overall studies that include randomized control trials, randomized cross-over studies and cluster-randomized trials designs that consider curative effect in children with ASD by probiotic supplements. We will search 6 databases: MEDLINE, Embase, Scopus, PubMed, The Cochrane Library, and Web of Science and we will perform a manual search the journal Autism and information of ongoing or unpublished studies. The Mixed Methods Appraisal Tool (MMAT) will be used to assess quality of articles and the Jadad scale will be used to assess for bias. Assessment of publication bias will be performed using funnel plots generated by Comprehensive Meta-Analysis (CMA) 3.0 software. Clarifying the evidence in this area will be important for future research directions when reformulating and promoting the therapeutic regime in the field. Ethics and dissemination: There are no human participants, data, or tissue being directly studied for the purposes of the review; therefore, ethics approval and consent to participate are not applicable. The results of this study will be presented at conferences and published in peer-reviewed journals. Registration and status: PROSPERO 2019 CRD42019132754.