Neutralizing antibodies have been designed to specifically target and bind to the receptor binding domain (RBD) of spike (S) protein to block severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus from attaching to angiotensin converting enzyme 2 (ACE2). This study reports a distinctive nanobody, designated as VHH21, that directly catalyzes the S-trimer into an irreversible transition state through postfusion conformational changes. Derived from camels immunized with multiple antigens, a set of nanobodies with high affinity for the S1 protein displays abilities to neutralize pseudovirion infections with a broad resistance to variants of concern of SARS-CoV-2, including SARS-CoV and BatRaTG13. Importantly, a super-resolution screening and analysis platform based on visual fluorescence probes was designed and applied to monitor single proteins and protein subunits. A spontaneously occurring dimeric form of VHH21 was obtained to rapidly destroy the S-trimer. Structural analysis via cryogenic electron microscopy revealed that VHH21 targets specific conserved epitopes on the S protein, distinct from the ACE2 binding site on the RBD, which destabilizes the fusion process. This research highlights the potential of VHH21 as an abzyme-like nanobody (nanoabzyme) possessing broad-spectrum binding capabilities and highly effective anti-viral properties and offers a promising strategy for combating coronavirus outbreaks.
Single-Domain Antibodies/immunology* ; Spike Glycoprotein, Coronavirus/metabolism* ; SARS-CoV-2/immunology* ; Animals ; Humans ; Antibodies, Neutralizing/immunology* ; Camelus ; COVID-19/immunology* ; Antibodies, Viral/immunology* ; Angiotensin-Converting Enzyme 2

Traumatic brain injury (TBI), as a significant central nervous system damage disease with high frequency in the world, leads to a huge number of patients with impaired health and lower quality of life every year. Lung injury is a common and dangerous consequence, which dramatically raises the mortality of patients. Discovering the pathophysiology of lung injury after TBI and discovering viable therapeutic targets has become an important need for clinical diagnosis and therapy. Neurotransmitters, as the fundamental chemical agents of the nervous system for signal transmission, not only govern neuronal activity and apoptosis in TBI but also significantly influence the pathophysiological mechanisms of lung injury subsequent to TBI. The imbalance is intricately linked to the onset and progression of lung damage. This paper systematically reviews the clinical characteristics and predominant pathogenesis of lung injury following TBI, emphasizing the role of key neurotransmitters, including glutamate (Glu), γ-aminobutyric acid (GABA), norepinephrine (NE), dopamine (DA), and acetylcholine (ACh), in lung injury post-TBI. It examines their influence on inflammatory response, vascular permeability, and pulmonary circulation function. Additionally, the paper evaluates the research advancements and potential applications of targeted therapeutic strategies for various neurotransmitter systems, such as receptor antagonists, transporter inhibitors, and neurotransmitter analogues. This research aims to offer a theoretical framework for clarifying the neural regulatory mechanisms of lung injury following TBI and to establish a basis for the development of novel therapeutic strategies and enhancement of the prognosis of the patients.
Humans ; Brain Injuries, Traumatic/metabolism* ; Neurotransmitter Agents/metabolism* ; Lung Injury/metabolism* ; gamma-Aminobutyric Acid/metabolism* ; Glutamic Acid/metabolism* ; Norepinephrine/metabolism* ; Dopamine/metabolism* ; Acetylcholine/metabolism*

OBJECTIVE: To compare the performance of machine learning models and traditional Cox regression model in predicting postoperative outcomes of patients with esophagogastric junction adenocarcinoma (AEG). METHODS: This study was conducted among 203 AEG patients with complete clinical and follow-up data, who were treated in our hospital between September, 2015 and October, 2020. The clinicopathological data of the patients were processed for analysis using R language package and divided into training and validation datasets at the ratio of 3:1. The Cox proportional hazards regression model and 4 machine learning models were constructed for analyzing the datasets. ROC curves, calibration curves and clinical decision curves (DCA) were plotted. Internal validation of the machine learning models was performed to assess their predictive efficacy. The predictive performance of each model was evaluated by calculating the area under the curve (AUC), and the model fitting was assessed using the calibration curve. RESULTS: For predicting 3-year survival based on the validation dataset, the AUC was 0.870 for Cox proportional hazard regression model, 0.901 for eXtreme Gradient Boosting (XGBoost), 0.791 for random forest, 0.832 for support vector machine, and 0.725 for multilayer perceptron; For predicting 5-year survival, the AUCs of these models were 0.915, 0.916, 0.758, 0.905, and 0.737, respectively. For internal validation, the AUCs of the 4 machine learning models decreased in the order of XGBoost (0.818), random forest (0.758), support vector machine (0.0.804), and multilayer perceptron (0.745). CONCLUSION The machine learning models show better predictive efficacy for survival outcomes of patients with AEG than Cox proportional hazard regression model, especially when proportional odds assumption or linear regression models are not applicable. XGBoost models have better performance than the other machine learning models, and the multi-layer perception model may have poor fitting results for a limited data volume.
Humans ; Adenocarcinoma ; Prognosis ; Machine Learning ; Esophagogastric Junction

Objective To investigate the effect of axial load test in Taylor spatial frame treatment of external fixation for tibia and fibula fractures.Methods A retrospective case-control study was conducted to analyze the clinical data of 36 patients with open fracture of tibia and fibula admitted to Tianjin Hospital from March 2015 to June 2017.There were 22 males and 14 females,aged 21-71 years[(46.1±14.2)years].All patients received Taylor spatial frame external fixation for tibia and fibula fracture within 1 week after injury.After operation,18 patients received axial load test(experiment group),and the other 18 did not(control group).When the value of axial load test was less than 5% in experiment group,the Taylor spatial frame was removed.The control group used traditional method to remove the Taylor spatial frame.Comparisons were made between the two groups in terms of treatment duration,total cost,re-fracture after Taylor spatial frame removal and incidence of stent-tract infection.Results All patients were followed up for 3-14 months with an average of 8.6 months.Compared with control group,the treatment duration[(36.17±11 .44)weeks vs.(44.50±9.16)weeks]and total cost[(93.7±7.9)thousand yuan vs.(120.1±10.6)thousand yuan]of experiment group were significantly lower(P＜0.05).In the experiment group,there was 0 patient with re-fracture and two patients with stent-tract infection,with the complication incidence of 11%,while there were two patients with re-fracture and three patients with stent-tract infection,with the complication incidence of 28% in the control group(P＞0.05).Conclusions After Taylor spatial frame external fixation for tibia and fibula fractures,regular axial load test can safely and timely guide the removal of Taylor spatial frame.It can reduce the treatment duration and cost compared with the traditional removal method,being safe and reliable.