Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

【Objective】 To study the relationship between the key influencing factors and the short, medium and long term blood demand, so as to provide basis for building a blood demand prediction model with less prediction error and practical guidance. 【Methods】 Through literature research, the influencing factors of blood demand were preliminarily determined. Questionnaires were designed and distributed to relevant experts, and factor analysis was carried out on the survey results to obtain key influencing factors through Delphi method. 【Results】 Through literature research, 19 influencing factors of clinical blood demand were obtained, including policy factors, medical service demand, medical technology level, regional population, population characteristics, population structure, medical resource, number of beds, culture, natural environment, operation, patients outside the region, blood use in different departments, blood infusion, time trend, emergencies and disasters, the condition of disasters, hospitals in disaster area, limited diagnosis and treatment ability. Through Delphi method and data analysis, six key factors affecting blood demand were obtained, namely sudden disaster, medical resource, environmental factor, population, bed number and blood infusion. 【Conclusion】 The influence of key factors on clinical blood demand was divided into multiple hierarchies. Blood infusion and sudden disaster were short-term influencing factors. Medical resource, population and number of beds were medium influencing factors. Environmental factor was long-term influencing factor. Short, medium and long-term influencing factors were interrelated, and have different impacts on clinical blood demand. Based on the interaction relationship, a three-dimensional mathematical model of influencing factors of clinical blood demand was established, which provided a preliminary research basis for building a blood demand prediction model with less prediction error and practical guidance.

【Objective】 To establish a prediction model of clinical blood demand in Suzhou urban area by ARIMA model, and to predict future clinical blood demand by sorting out the historical data, so as to guide the reasonable collection and scientific deployment of blood resources, and achieve the balance of clinical blood supply and demand. 【Methods】 The monthly data of clinical use of plasma components in Suzhou city from 2009 to 2019 were obtained, and analyzed by SPSS26 software and ARIMA model. Through model identification, parameter estimation and optimal model test, the optimal model for clinical blood prediction was determined and used to predict the clinical consumption of plasma components from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model was ARIMA(0, 1, 1)(0, 1, 1)12. The values of ACF autocorrelation function and PACF partial autocorrelation function of residual were both within 95%CI. Meanwhile, the Yang-Box Q statistic value was 11.596, P>0.05, which passed the white noise test. The predicted values of clinical consumption of plasma components in Suzhou urban area from January to November 2020 were all within 95%CI, consistent with the trend of actual values, with small mean relative error(7.9%) and good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction on clinical use of plasma components in Suzhou city, and provide reference for reasonable collection, preparation and scientific deployment.

【Objective】 To establish an ARIMA model suitable for clinical platelet demand prediction in Suzhou, which can be used as reference to predict future clinical platelet demand and provide scientific basis for platelet collection, preparation, stock management and clinical deployment for blood banks, so as to achieve the maximum balance between platelets supply and demand . 【Methods】 The data of platelet consumption in Suzhou from 2009 to 2019 were collected and analyzed by SPSS 26 software, Time series analysis method was used to establish the ARIMA model. The model was further optimized through model identification, parameter estimation and optimal model test, and then used to predict clinical platelet consumption from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model for the prediction of platelet clinical demand was ARIMA (0, 1, 1) (0, 1, 1) 12. The ACF autocorrelation function value and PACF partial autocorrelation function value of the residuals were within 95% CI. Meanwhile, the LJUNG BOX test was 13.982 (P>0.05), indicating that there was no autocorrelation in the residuals. The trend of the curve between the predicted and actual value was basically the same(except for February 2020), and the predicted values were within 95% CI, with the average relative error of 7.22%, which was lower than 10%, showing good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction of clinical platelets demand in Suzhou, and can provide basis for reasonable collection, preparation and deployment of platelets.

To enhance the specificity of anti-TNF-α single chain Fv antibody (TNF-scFv) to inflamed site, we constructed a bispecific antibody BsDb that targets TNF-α and ED-B-containing fibronectin (B-FN) by covalently linking TNF-scFv and the anti-ED-B scFv L19 at the gene level via a flexible peptide linker deriving from human serum albumin. BsDb was successfully secreted from Pichia pastoris as functional protein, identified by immunoblotting, and purified to homogeneity with affinity chromatography. BsDb retained the immunoreactivity of its original antibodies TNF-scFv and L19, and showed a marked gain in antigen-binding affinity and in TNF-α-neutralizing ability, when compared to TNF-scFv and L19 that were produced in Escherichia coli. In the adjuvant-induced arthritis (AIA) mice model, BsDb showed selective accumulation and retention in the inflamed paws but rapid clearance from blood, resulting in high arthritic paw to blood ratios. These data indicate that BsDb is endowed with high specificity to inflamed site and low toxicity to normal tissues and holds great potential for in vivo application for the targeted therapy of RA and other chronic inflammatory diseases.
Animals ; Antibodies, Bispecific ; biosynthesis ; immunology ; Antibodies, Neutralizing ; biosynthesis ; immunology ; Escherichia coli ; Fibronectins ; chemistry ; immunology ; Humans ; Mice ; Single-Chain Antibodies ; biosynthesis ; immunology ; Tumor Necrosis Factor-alpha ; immunology