Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

BACKGROUND: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. OBJECTIVE: Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. METHODS: We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. RESULTS: We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. CONCLUSIONS: The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.07.27.23293272.

Sodium alginate/sodium lignosulfonate hydrogel based on inert Ca2+ activation for water conservation and growth promotion.

Soil degradation has become a major global problem owing to the rapid development of agriculture. The problems of soil drought and decreased soil fertility caused by soil degradation severely affect the development of the agricultural and forestry industries. In this study, we designed sodium alginate (SA)/sodium lignosulfonate (SLS) hydrogel based on the activation and crosslinking of inert Ca2+. CaCO3 and SA were mixed, and then, inert Ca2+ was activated to prepare a gel with a stable structure and a uniform interior and exterior. The crosslinking activated by inert Ca2+ enhanced the stability of the hydrogel, and the optimal swelling rate of the hydrogel reached 28.91 g/g, thereby effectively improving the water-holding capacity of the soil (77.6-108.83 g/kg). SLS was degraded into humic acid (HA) and gradually released, demonstrating a positive growth-promoting effect in plant growth experiments. The SA/SLS hydrogel can be used for soil water retention and mitigation to significantly decrease the water loss rate of soil. This study will assist in addressing soil drought and fertility loss.

A jingmenvirus RNA-dependent RNA polymerase structurally resembles the flavivirus counterpart but with different features at the initiation phase.

Jingmenviruses are a category of emerging segmented viruses that have garnered global attention in recent years, and are close relatives of the flaviviruses in the Flaviviridae family. One of their genome segments encodes NSP1 homologous to flavivirus NS5. NSP1 comprises both the methyltransferase (MTase) and RNA-dependent RNA polymerase (RdRP) modules playing essential roles in viral genome replication and capping. Here we solved a 1.8-Å resolution crystal structure of the NSP1 RdRP module from Jingmen tick virus (JMTV), the type species of jingmenviruses. The structure highly resembles flavivirus NS5 RdRP despite a sequence identity less than 30%. NSP1 RdRP enzymatic properties were dissected in a comparative setting with several representative Flaviviridae RdRPs included. Our data indicate that JMTV NSP1 produces characteristic 3-mer abortive products similar to the hepatitis C virus RdRP, and exhibits the highest preference of terminal initiation and shorter-primer usage. Unlike flavivirus NS5, JMTV RdRP may require the MTase for optimal transition from initiation to elongation, as an MTase-less NSP1 construct produced more 4-5-mer intermediate products than the full-length protein. Taken together, this work consolidates the evolutionary relationship between the jingmenvirus group and the Flaviviridae family, providing a basis to the further understanding of their viral replication/transcription process.