Identification of a novel linear B-cell epitope in porcine deltacoronavirus nucleocapsid protein.

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 28QFRGNGVPLNSAIKPVE44 (EP-4D3) in the N-terminal of PDCoV N protein was identified as the minimal linear epitope for binding mAb 4D3. And the EP-4D3 epitope's amino acid sequence homology study revealed that PDCoV strains are substantially conserved, with the exception of the Alanine43 substitution Valine43 in the China lineage, the Early China lineage, and the Thailand, Vietnam, and Laos lineage. The epitope sequences shared high similarity (94.1%) with porcine coronavirus HKU15-155 (PorCoV HKU15), Asian leopard cats coronavirus (ALCCoV), sparrow coronavirus HKU17 (SpCoV HKU17), and sparrow deltacoronavirus. In contrast, the epitope sequences shared a very low homology (11.8 to 29.4%) with other porcine CoVs (PEDV, TGEV, PRCV, SADS-CoV, PHEV). Overall, the study will enrich the biological function of PDCoV N protein and provide foundational data for further development of diagnostic applications. KEY POINTS: • Three monoclonal antibodies against PDCoV N protein were prepared. • Discovery of a novel B-cell liner epitope (28QFRGNGVPLNSAIKPVE44) of PDCoV N protein. • The epitope EP-4D3 was conserved among PDCoV strains.

Human umbilical cord mesenchymal stem cells in diabetes mellitus and its complications: applications and research advances.

Diabetes mellitus and its complications pose a major threat to global health and affect the quality of life and life expectancy of patients. Currently, the application of traditional therapeutic drugs for diabetes mellitus has great limitations and can only temporarily control blood glucose but not fundamentally cure it. Mesenchymal stem cells, as pluripotent stromal cells, have multidirectional differentiation potential, high self-renewal, immune regulation, and low immunogenicity, which provide a new idea and possible development direction for diabetes mellitus treatment. Regenerative medicine with mesenchymal stem cells treatment as the core treatment will become another treatment option for diabetes mellitus after traditional treatment. Recently, human umbilical cord mesenchymal stem cells have been widely used in basic and clinical research on diabetes mellitus and its complications because of their abundance, low ethical controversy, low risk of infection, and high proliferation and differentiation ability. This paper reviews the therapeutic role and mechanism of human umbilical cord mesenchymal stem cells in diabetes mellitus and its complications and highlights the challenges faced by the clinical application of human umbilical cord mesenchymal stem cells to provide a more theoretical basis for the application of human umbilical cord mesenchymal stem cells in diabetes mellitus patients.

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study.

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.

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

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 < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit. 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.

Comprehensive codon usage analysis of porcine deltacoronavirus.

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.

Emergence, phylogeography, and adaptive evolution of mpox virus.

Mpox (Monkeypox) is a zoonotic disease caused by mpox virus (MPXV). A multi-country MPXV outbreak in non-endemic demographics was identified in May 2022. A systematic evaluation of MPXV evolutionary trajectory and genetic diversity could be a timely addition to the MPXV diagnostics and prophylaxis. Herein, we integrated a systematic evolution analysis including phylogenomic and phylogeographic, followed by an in-depth analysis of the adaptive evolution and amino acid variations in type I interferon binding protein (IFNα/ßBP). Mutations in IFNα/ßBP protein may impair its binding capacity, affecting the MPXV immune evasion strategy. Based on the equilibrated data, we found an evolutionary rate of 7.75 × 10 - 5 substitutions/site/year, and an earlier original time (2021.25) of the clade IIb. We further discovered significant genetic variations in MPXV genomes from different regions and obtained six plausible spread trajectories from its intricate viral flow network, implying that North America might have acted as a bridge for the spread of MPXV from Africa to other continents. We identified two amino acids under positive selection in the Rifampicin resistance protein and extracellular enveloped virus (EEV) type-I membrane glycoprotein, indicating a role in adaptive evolution. Our research sheds light on the emergence, dispersal, and adaptive evolution of MPXV, providing theoretical support for mitigating and containing its expansion.

Emergence and adaptive evolution of Nipah virus.

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.