ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Langerhans cell histiocytosis of bone is a rare tumor disease characterized by the large accumulation of CD1a⁺ and CD207⁺ dendritic cells in tissues of unknown cause.It mainly occurs in children aged 1-4 years old,with incidences of 4-6 per million in children and 1-2 per million in adults.Due to its low incidence,diverse clinical manifestations,and no obvious specificity of imaging manifestations,the definitive diagnosis and early treatment of this type of tumor are challenging.In this paper,we report 8 cases of Langerhans cell histiocytosis of bone and review the relevant literature published in the past five years to summarize the clinical characteristics,pathological features,diagnosis,treatment,and prognosis of this disease.
Humans ; Bone Diseases/therapy* ; Histiocytosis, Langerhans-Cell/therapy*

Objective To construct a prognosis prediction model of primary liver cancer after surgical treatment based on synthetic minority over-sampling technique(SMOTE)algorithm and machine learning model.Methods A retrospective cohort study was conducted on 4 297 patients with primary liver cancer from the surveillance,epidemiology,and end results(SEER)database.One-Hot Encoding and Multiple Imputation were used to preprocess the collect data,and SMOTE algorithm was employed to solve the imbalance of data categories.The obtained clinical variables were included in the machine learning model.Based on decision tree(DT),random forest(RF),gradient boosting decision tree(GBDT)and eXtreme Gradient Boosting(XGBoost),a prognostic prediction model(SMOTE+DT/RF/GBDT/XGBoost)was build,and then the best prediction model was determined by comparing the performance of various models.Finally,a prognostic analysis system for primary liver cancer was developed based on the optimal model,which was then visualized.Results The combination model SMOTE+RF showed the best predictive performance,with higher area under the curve(0.895),accuracy(0.811)and precision(0.806)than those of other models in receiver operating characteristic curve(ROC)analysis.Conclusion The SMOTE+RF prognostic prediction model can effectively predict the survival outcome of patients with primary liver cancer.

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*

Child ; Humans ; Manometry ; Esophagus/physiology*

Mitosis is important for cell proliferation in eukaryotes, and chromosome replication and accurate separation are essential for cell division. Supervillin is a membrane and microfilament actin binding protein. Previous studies have found that it regulates the dynamic changes of the cortical distribution of F-actin and myosin II in cytokinesis, thus ensuring the correct distribution of the contraction ring and participating in the final completion of cytoplasm divisions. But it is not clear whether it functions during metaphase. Supervillin has several splicing isomers, among which supervillin isoform 4 (SV4) is the largest splicing isomer. In this study, the expression of SV4 in cells was reduced by the RNA interference method, and the dynamic process of mitosis and the morphology of astral spindles were detected and observed by real-time microscopy and immunofluorescence staining, and the potential molecular mechanism of SV4 in mitosis was analyzed. The results showed abnormal cell divisions after SV4 reduction: delayed transition from metaphase to anaphase (P<0.001), abnormal assembly of microtubules, a twofold-increase of the number of cells with multipolar spindles, and decreased γ-tubulin signaling in the centrosome (P<0.001). Through GST pull-down and mass spectrometry experiments, we found that SV4 and Aurora A bind to each other, and SV4 regulates the localization and activation of Aurora A in the centrosome during mitosis. In summary, supervillin plays an important role in mitosis. The isomer SV4 regulates spindle integrity and γ-tubulin recruitment by interacting with Aurora A and recruiting it for proper localization and activation in the centrosome during the metaphase, thus promoting the correct assembly of bipolar spindles and ensuring the accurate separation of chromosomes and the smooth progress of mitosis.

OBJECTIVES: To investigate the clinical treatment outcomes and the changes of the outcomes over time in extremely preterm twins in Guangdong Province, China. METHODS: A retrospective analysis was performed for 269 pairs of extremely preterm twins with a gestational age of <28 weeks who were admitted to the department of neonatology in 26 grade A tertiary hospitals in Guangdong Province from January 2008 to December 2017. According to the admission time, they were divided into two groups: 2008-2012 and 2013-2017. Besides, each pair of twins was divided into the heavier infant and the lighter infant subgroups according to birth weight. The perinatal data of mothers and hospitalization data of neonates were collected. The survival rate of twins and the incidence rate of complications were compared between the 2008-2012 and 2013-2017 groups. RESULTS: Compared with the 2008-2012 group, the 2013-2017 group (both the heavier infant and lighter infant subgroups) had lower incidence rates of severe asphyxia and smaller head circumference at birth (P<0.05). The mortality rates of both of the twins, the heavier infant of the twins, and the lighter infant of the twins were lower in the 2013-2017 group compared with the 2008-2012 group (P<0.05). Compared with the 2008-2012 group, the 2013-2017 group (both the heavier infant and lighter infant subgroups) had lower incidence rates of pulmonary hemorrhage, patent ductus arteriosus (PDA), periventricular-intraventricular hemorrhage (P-IVH), and neonatal respiratory distress syndrome (NRDS) and a higher incidence rate of bronchopulmonary dysplasia (P<0.05). CONCLUSIONS There is a significant increase in the survival rate over time in extremely preterm twins with a gestational age of <28 weeks in the 26 grade A tertiary hospitals in Guangdong Province. The incidences of severe asphyxia, pulmonary hemorrhage, PDA, P-IVH, and NRDS decrease in both the heavier and lighter infants of the twins, but the incidence of bronchopulmonary dysplasia increases. With the improvement of diagnosis and treatment, the multidisciplinary collaboration between different fields of fetal medicine including prenatal diagnosis, obstetrics, and neonatology is needed in the future to jointly develop management strategies for twin pregnancy.
Bronchopulmonary Dysplasia/epidemiology* ; Female ; Gestational Age ; Humans ; Infant ; Infant, Extremely Premature ; Infant, Newborn ; Pregnancy ; Respiratory Distress Syndrome, Newborn/epidemiology* ; Retrospective Studies ; Treatment Outcome