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Porcine deltacoronavirus (PDCoV) is an emerging swine enteropathogenic coronavirus that caused diarrhea and/or vomiting in neonatal piglets worldwide. Coronaviruses nucleocapsid (N) protein is the most conserved structural protein for viral replication and possesses good antigenicity. In this study, three monoclonal antibodies (mAbs), 3B4, 4D3, and 4E3 identified as subclass IgG2aκ were prepared using the lymphocytic hybridoma technology against PDCoV N protein. Furthermore, the B-cell epitope recognized by mAb 4D3 was mapped by dozens of overlapping truncated recombinant proteins based on the western blotting. The polypeptide

BACKGROUND Patients with high myopia have an increased lifetime risk of complications. The prevalence patterns of high myopia in children and adolescents in southern China are unclear. Early identification of high-risk individuals is critical for reducing the occurrence and development of high myopia and avoiding the resulting complications. OBJECTIVE This study aimed to determine the prevalence of high myopia in children and adolescents in southern China via real-world screening data and to predict its onset by studying the risk factors for high myopia based on machine learning. METHODS This retrospective school-based study was conducted in 13 cities with different gross domestic products in southern China. Through data acquisition and filtering, we analyzed the prevalence of high myopia and its association with age, school stage, gross domestic product, and risk factors. A random forest algorithm was used to predict high myopia among schoolchildren and then assessed in an independent hold-out group. RESULTS There were 1,285,609 participants (mean age 11.80, SD 3.07, range 6-20 years), of whom 658,516 (51.2%) were male. The overall prevalence of high myopia was 4.48% (2019), 4.88% (2020), and 3.17% (2021), with an increasing trend from the age of 11 to 17 years. The rates of high myopia increased from elementary schools to high schools but decreased at all school stages from 2019 to 2021. The coastal and southern cities had a higher proportion of high myopia, with an overall prevalence between 2.60% and 5.83%. Age, uncorrected distance visual acuity, and spherical equivalents were predictive factors for high myopia onset in schoolchildren. The random forest algorithm achieved a high accuracy of 0.948. The area under the receiver operator characteristic curve (AUC) was 0.975. Both indicated sufficient model efficacy. The performance of the model was validated in an external test with high accuracy (0.971) and a high AUC (0.957). CONCLUSIONS High myopia had a high incidence in Guangdong Province. Its onset in children and adolescents was well predicted with the random forest algorithm. Efficient use of real-world data can contribute to the prevention and early diagnosis of high myopia.

Background This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA). Methods A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P Results A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706–0.789) in the training dataset, and 0.712 (95% CI, 0.642–0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful. Conclusion The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.

Additional file 3: SQL code for data extraction.

Additional file 2: Table S2. Univariate logistic regression analysis.

Additional file 1: Table S1. Characteristics of patients in the training and validation datasets.

Since its first emergence in 1998 in Malaysia, Nipah virus (NiV) has become a great threat to domestic animals and humans. Sporadic outbreaks associated with human‐to‐human transmission caused hundreds of human fatalities. Here, we collected all available NiV sequences and combined phylogenetics, molecular selection, structural biology and receptor analysis to study the emergence and adaptive evolution of NiV. NiV can be divided into two main lineages including the Bangladesh and Malaysia lineages. We formly confirmed a significant association with geography which is probably the result of long‐term evolution of NiV in local bat population. The two NiV lineages differ in many amino acids; one change in the fusion protein might be involved in its activation via binding to the G protein. We also identified adaptive and positively selected sites in many viral proteins. In the receptor‐binding G protein, we found that sites 384, 386 and especially 498 of G protein might modulate receptor‐binding affinity and thus contribute to the host jump from bats to humans via the adaption to bind the human ephrin‐B2 receptor. We also found that site 1645 in the connector domain of L was positive selected and involved in adaptive evolution; this site might add methyl groups to the cap structure present at the 5′‐end of the RNA and thus modulate its activity. This study provides insight to assist the design of early detection methods for NiV to assess its epidemic potential in humans.

Breath analysis is of great significance for the early diagnosis of diabetic ketoacidosis. Gas sensors with simple structure and low cost have become candidates for breath analysis. The detection range and reliability of the device play an important role in the actual application. In this work, we used Bi1-xSrxFeO3 (x = 0.2, 0.4, 0.6, 0.8) prepared by sol-gel method to fabricate Ce0.8Gd0.2O1.95 (GDC)-based mixed potential type acetone sensors, and obtained satisfactory sensing performance. The amount of Sr substitution in sensing materials could change the content of deficient oxygen inside the material and the electrochemical catalytic activity of the material, thereby improving the sensitivity of the sensor. Mixed potential sensing mechanism and the reason for sensitivity segmentation phenomenon were also explained. In addition, we did clinical tests for the sensor towards the exhalation of diabetic patients and healthy people, which displayed the potential of the sensor used for breath analysis.

Graphical abstract, Porcine deltacoronavirus (PDCoV) is a newly identified coronavirus of pigs that was first reported in Hong Kong in 2012. Since then, many PDCoV isolates have been identified worldwide. In this study, we analyzed the codon usage pattern of the S gene using complete coding sequences and complete PDCoV genomes to gain a deeper understanding of their genetic relationships and evolutionary history. We found that during evolution three groups evolved with a relatively low codon usage bias (effective number of codons (ENC) of 52). The factors driving bias were complex. However, the primary element influencing the codon bias of PDCoVs was natural selection. Our results revealed that different natural environments may have a significant impact on the genetic characteristics of the strains. In the future, more epidemiological surveys are required to examine the factors that resulted in the emergence and outbreak of this virus.