Preparation and characterization of mouse-derived monoclonal antibodies against the hemagglutinin of the H1N1 influenza virus.

H1N1 influenza virus is a significant global public health concern. Monoclonal antibodies (mAbs) targeting specific viral proteins such as hemagglutinin (HA) have become an important therapeutic strategy, offering highly specific targeting to block viral transmission and infection. This study focused on the development of mAbs targeting HA of the A/Victoria/2570/2019 (H1N1pdm09, VIC-19) strain by utilizing hybridoma technology to produce two mAbs with high binding capacity. Notably, mAb 2B2 has demonstrated a strong affinity for HA proteins in recent H1N1 influenza vaccine strains. In vitro assessments showed that both mAbs exhibited broad-spectrum hemagglutination inhibition and potent neutralizing effects against various vaccine strains of H1N1pdm09 viruses. 2B2 was also effective in animal models, offering both preventive and therapeutic protection against infections caused by recent H1N1 strains, highlighting its potential for clinical application. By individually co-cultivating each of the aforementioned mAbs with the virus in chicken embryos, four amino acid substitution sites in HA (H138Q, G140R, A141E/V, and D187E) were identified in escape mutants, three in the antigenic site Ca2, and one in Sb. The identification of such mutations is pivotal, as it compels further investigation into how these alterations could undermine the binding efficacy and neutralization capacity of antibodies, thereby impacting the design and optimization of mAb therapies and influenza vaccines. This research highlights the necessity for continuous exploration into the dynamic interaction between viral evolution and antibody response, which is vital for the formulation of robust therapeutic and preventive strategies against influenza.

OHCCPredictor: an online risk stratification model for predicting survival duration of older patients with hepatocellular carcinoma.

BACKGROUND: Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS). METHODS: 2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA). RESULTS: These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803-0.845); internal validation set: AUC = 0.847 (95%CI 0.818-0.876); external validation set: AUC = 0.732 (95%CI 0.521-0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790-0.837); internal validation set: AUC = 0.844 (95%CI 0.812-0.876); external validation set: AUC = 0.780 (95%CI 0.674-0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806-0.872); internal validation set: AUC = 0.800 (95%CI 0.751-0.849); external validation set: AUC = 0.821 (95%CI 0.727-0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function ( https://juntaotan.shinyapps.io/dynnomapp_hcc/ ). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC. CONCLUSIONS: Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading. Source codes and datasets are available at our GitHub repository (https://github.com/RobotvisionLab/COVID-19-severity-grading.git).

A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study.

BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users. CONCLUSION: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.

Tripartite motif-containing protein 46 accelerates influenza A H7N9 virus infection by promoting K48-linked ubiquitination of TBK1.

BACKGROUND: Avian influenza A H7N9 emerged in 2013, threatening public health and causing acute respiratory distress syndrome, and even death, in the human population. However, the underlying mechanism by which H7N9 virus causes human infection remains elusive. METHODS: Herein, we infected A549 cells with H7N9 virus for different times and assessed tripartite motif-containing protein 46 (TRIM46) expression. To determine the role of TRIM46 in H7N9 infection, we applied lentivirus-based TRIM46 short hairpin RNA sequences and overexpression plasmids to explore virus replication, and changes in type I interferons and interferon regulatory factor 3 (IRF3) phosphorylation levels in response to silencing and overexpression of TRIM46. Finally, we used Co-immunoprecipitation and ubiquitination assays to examine the mechanism by which TRIM46 mediated the activity of TANK-binding kinase 1 (TBK1). RESULTS: Type I interferons play an important role in defending virus infection. Here, we found that TRIM46 levels were significantly increased during H7N9 virus infection. Furthermore, TRIM46 knockdown inhibited H7N9 virus replication compared to that in the control group, while the production of type I interferons increased. Meanwhile, overexpression of TRIM46 promoted H7N9 virus replication and decrease the production of type I interferons. In addition, the level of phosphorylated IRF3, an important interferon regulatory factor, was increased in TRIM46-silenced cells, but decreased in TRIM46 overexpressing cells. Mechanistically, we observed that TRIM46 could interact with TBK1 to induce its K48-linked ubiquitination, which promoted H7N9 virus infection. CONCLUSION: Our results suggest that TRIM46 negatively regulates the human innate immune response against H7N9 virus infection.