Advancements in bioscavenger mediated detoxification of organophosphorus poisoning.

Background: Organophosphorus compounds, widely used in agriculture and industry, pose a serious threat to human health due to their acute neurotoxicity. Although traditional interventions for organophosphate poisoning are effective, they often come with significant side effects. Objective: This paper aims to evaluate the potential of enzymes within biological organisms as organophosphorus bioclearing agents. It analyses the technical challenges in current enzyme research, such as substrate specificity, stereoselectivity, and immunogenicity, while exploring recent advancements in the field. Methods: A comprehensive review of literature related to detoxifying enzymes or proteins was conducted. Existing studies on organophosphorus bioclearing agents were summarised, elucidating the biological detoxification mechanisms, with a particular focus on advancements in protein engineering and novel delivery methods. Results: Current bioclearing agents can be categorised into stoichiometric and catalytic bioclearing agents, both of which have shown some success in preventing organophosphate poisoning. Technological advancements have significantly improved various properties of bioclearing agents, yet challenges remain, particularly in substrate specificity, stereoselectivity, and immunogenicity. Future research will focus on expanding the substrate spectrum, enhancing catalytic efficiency, prolonging in vivo half-life, and developing convenient administration methods. Conclusion: With the progression of clinical trials, bioclearing agents are expected to become widely used as a new generation of therapeutic organophosphate detoxifiers.

Less Acetabular Coverage Predicts the Failure of Core Decompression for Osteonecrosis of the Femoral Head: A Retrospective Cohort Study.

OBJECTIVE: It is unclear whether less acetabular coverage is associated with the failure of core decompression (CD) for osteonecrosis of the femoral head (ONFH). This study aimed to investigate the clinical outcomes of CD for ONFH with small- or medium-sized pre-collapse lesions, and determine what factors, especially acetabular anatomical parameters, predict the failure of CD. METHODS: Between January 2010 and December 2022, we retrospectively reviewed 269 consecutive CDs in 188 patients diagnosed with ONFH with small- or medium-sized pre-collapse lesions. The Kaplan-Meier method was used to evaluate the survival rate of CD for ONFH with progression of collapse or conversion to total hip arthroplasty (THA) as the endpoint. Univariate and multivariate logistic regression analyses were conducted to identify the potential risk factors for the failure of CD. Receiver operating characteristic (ROC) curve analysis was further performed with conversion to THA as the endpoint to determine the predictive value of these factors. RESULTS: The overall 5-year survival rate of CD for ONFH with small- or medium-sized pre-collapse lesions was 74.3% (95% confidence interval (CI) 69.0%-81.1%) with progression of collapse as the endpoint and 83.9% (95% CI 79.3%-88.7%) with conversion to THA as the endpoint. Univariate logistic regression analysis showed that bilateral affected hips was significantly associated with progression of collapse, and center-edge angle (CEA), sharp angle, acetabular head index (AHI), as well as acetabular depth ratio (ADR) were significantly associated with both progression of collapse and conversion to THA. Multivariate logistic regression analysis further indicated that CEA and AHI were independent risk factors for both progression of collapse and conversion to THA. ROC curve analysis with conversion to THA as the endpoint revealed that the cutoff values for CEA and AHI were 26.8° (sensitivity = 74.4%, specificity = 78.6%, area under the curve (AUC) = 0.809) and 79.8 (sensitivity = 78.4%, specificity = 73.8%, AUC = 0.818), respectively. CONCLUSIONS: CD showed satisfactory clinical outcomes for ONFH with small- or medium-sized pre-collapse lesions where less acetabular coverage with a CEA < 26.8° or AHI < 79.8 was identified as an independent risk factor for the failure of CD.

Research on Detection of Ultra-Low Concentration Anthrax Protective Antigen Using Graphene Field-Effect Transistor Biosensor.

BACKGROUND: Protective antigen (PA) is an important biomarker for the early diagnosis of anthrax, and the accurate detection of protective antigen under extremely low concentration conditions has always been a hot topic in the biomedical field. To complete the diagnosis of anthrax in a timely manner, it is necessary to detect PA at extremely low concentrations, as the amount of PA produced in the early stage of anthrax invasion is relatively small. Graphene field-effect transistor (Gr-FET) biosensors are a new type of material for preparing biosensors, with the advantages of a short detection time and ultra-low detection limit. METHODS: The effect of different concentrations of diluents on the affinity of PA monoclonal antibodies was determined via an ELISA experiment. Combined with the Debye equation, 0.01 × PBS solution was finally selected as the diluent for the experiment. Then, a PA monoclonal antibody was selected as the bio-recognition element to construct a Gr-FET device based on CVD-grown graphene, which was used to detect the concentration of PA while recording the response time, linear range, detection limit, and other parameters. RESULTS: The experimental results showed that the biosensor could quickly detect PA, with a linear range of 10 fg/mL to 100 pg/mL and a detection limit of 10 fg/mL. In addition, the biosensor showed excellent specificity and repeatability. CONCLUSIONS: By constructing a Gr-FET device based on CVD-grown graphene and selecting a PA monoclonal antibody as the bio-recognition element, a highly sensitive, specific, and repeatable Gr-FET biosensor was successfully prepared for detecting extremely low concentrations of anthrax protective antigen (PA). This biosensor is expected to have a wide range of applications in clinical medicine and biological safety monitoring.

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.

The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.