Tracheal intubation in patients with Pierre Robin sequence: development, application, and clinical value based on a 3-dimensional printed simulator

Background: The main clinical manifestations of patients with Pierre Robin sequence (PRS) include micrognathia, the glossoptosis and dyspnoea. The difficulty of tracheal intubation (TI) in such patients is increased.Objective: The purpose of the study was to evaluate the reliability and efficacy of the PRS simulator.Methods: A PRS simulator was developed by using 3-dimensional (3D) printing technology under computer-aided design. A total of 12 anaesthesiologists each trained 5 times for TI on the PRS Training Simulator-1 and recorded the simulation time. After the training, they were randomly divided into three groups with a total of 12 nontrained anaesthesiologists, and the simulation was completed on PRS Simulator-2, 3 and 4. The simulation time was recorded, and the performance was evaluated by three chief anaesthesiologists. Then, all 24 anaesthesiologists completed the questionnaire.Results: A PRS simulator developed by 3D printing was used to simulate the important aspects of TI. The average number of years worked was 6.3 ± 3.1 years, and 66.7% were female. The time for the 12 anaesthesiologists to complete the training gradually decreased (p < 0.01). Compared with the trained anaesthesiologists, the simulation time of TI in the nontrained anaesthesiologists was much longer (all p < 0.01). In addition, the simulation performance of the trained anaesthesiologists was relatively better (all p < 0.01).Conclusion: The reliability and efficacy of the PRS simulator is herein preliminarily validated, and it has potential to become a teaching and training tool for anaesthesiologists

MSMEG_2731, an Uncharacterized Nucleic Acid Binding Protein from Mycobacterium smegmatis, Physically Interacts with RPS1

While the M. smegmatis genome has been sequenced, only a small portion of the genes have been characterized experimentally. Here, we purify and characterize MSMEG_2731, a conserved hypothetical alanine and arginine rich M. smegmatis protein. Using ultracentrifugation, we show that MSMEG_2731 is a monomer in vitro. MSMEG_2731 exists at a steady level throughout the M. smegmatis life-cycle. Combining results from pull-down techniques and LS-MS/MS, we show that MSMEG_2731 interacts with ribosomal protein S1. The existence of this interaction was confirmed by co-immunoprecipitation. We also show that MSMEG_2731 can bind ssDNA, dsDNA and RNA in vitro. Based on the interactions of MSMEG_2731 with RPS1 and RNA, we propose that MSMEG_2731 is involved in the transcription-translation process in vivo

Emergency tracheal intubation in 202 patients with COVID-19 in Wuhan, China:lessons learnt and international expert recommendations

Tracheal intubation in coronavirus disease 2019 (COVID-19) patients creates a risk to physiologically compromised patients and to attending healthcare providers. Clinical information on airway management and expert recommendations in these patients are urgently needed. By analysing a two-centre retrospective observational case series from Wuhan, China, a panel of international airway management experts discussed the results and formulated consensus recommendations for the management of tracheal intubation in COVID-19 patients. Of 202 COVID-19 patients undergoing emergency tracheal intubation, most were males (n=136; 67.3%) and aged 65 yr or more (n=128; 63.4%). Most patients (n=152; 75.2%) were hypoxaemic (Sao2 <90%) before intubation. Personal protective equipment was worn by all intubating healthcare workers. Rapid sequence induction (RSI) or modified RSI was used with an intubation success rate of 89.1% on the first attempt and 100% overall. Hypoxaemia (Sao2 <90%) was common during intubation (n=148; 73.3%). Hypotension (arterial pressure <90/60 mm Hg) occurred in 36 (17.8%) patients during and 45 (22.3%) after intubation with cardiac arrest in four (2.0%). Pneumothorax occurred in 12 (5.9%) patients and death within 24 h in 21 (10.4%). Up to 14 days post-procedure, there was no evidence of cross infection in the anaesthesiologists who intubated the COVID-19 patients. Based on clinical information and expert recommendation, we propose detailed planning, strategy, and methods for tracheal intubation in COVID-19 patients

In Situ Monitoring of Corrosion under Insulation Using Electrochemical and Mass Loss Measurements

Corrosion under insulation (CUI) refers to the external corrosion of piping and vessels when they are encapsulated in thermal insulation. To date, very limited information (especially electrochemical data) is available for these “difficult-to-test” CUI conditions. This study was aimed at developing a novel electrochemical sensing method for in situ CUI monitoring and analysis. Pt-coated Ti wires were used to assemble a three-electrode electrochemical cell over a pipe surface covered by thermal insulation. The CUI behavior of X70 carbon steel (CS) and 304 stainless steel (SS) under various operating conditions was investigated using mass loss, linear polarization resistance (LPR), and electrochemical impedance spectroscopy (EIS) measurements. It was found that both the consecutive wet and dry cycles and cyclic temperatures accelerated the progression of CUI. LPR and EIS measurements revealed that the accelerated CUI by thermal cycling was due to the reduced polarization resistance and deteriorated corrosion film. Enhanced pitting corrosion was observed on all tested samples after thermal cycling conditions, especially for CS samples. The proposed electrochemical technique demonstrated the ability to obtain comparable corrosion rates to conventional mass loss data. In addition to its potential for in situ CUI monitoring, this design could be further applied to rank alloys, coatings, and inhibitors under more complex exposure conditions.</jats:p

Three dimensional evaluation on soil improvement effect of saline alkali soil in Yellow River Delta

Abstract It is necessary to evaluate the effect of soil improvement after land consolidation for productivity improvement, but the current evaluation method is relatively single. In this paper, based on the data of soil salt content from monitoring and sampling, the statistical characteristics were analyzed by SPSS software, and the three-dimensional distribution map was constructed by using GMS10.0 software for IDW interpolation. The results showed that the soil salt content in 2019 decreased significantly by 31.29% compared with that in 2018 from the characteristics of cultivated layer, and the effect of soil improvement was obvious. From the profile characteristics, the soil salt content in the study area is characterized by “surface accumulation”. The salt content of soil profile decreased significantly at maize maturity stage compared with that before sowing. The decrease rates of soil salt content at different depths were 70.11% (0-20cm), 77.43% (20-40cm), 77.31% (40-60cm), 83.34% (60-80cm) and 65.26% (80-100cm), respectively. The decrease rate of 60-80cm soil layer was the largest. The results can be used as a reference for single index or comprehensive index three-dimensional visualization evaluation of soil improvement effect.</jats:p

Explicit Image Caption Reasoning: Generating Accurate and Informative Captions for Complex Scenes with LMM

The rapid advancement of sensor technologies and deep learning has significantly advanced the field of image captioning, especially for complex scenes. Traditional image captioning methods are often unable to handle the intricacies and detailed relationships within complex scenes. To overcome these limitations, this paper introduces Explicit Image Caption Reasoning (ECR), a novel approach that generates accurate and informative captions for complex scenes captured by advanced sensors. ECR employs an enhanced inference chain to analyze sensor-derived images, examining object relationships and interactions to achieve deeper semantic understanding. We implement ECR using the optimized ICICD dataset, a subset of the sensor-oriented Flickr30K-EE dataset containing comprehensive inference chain information. This dataset enhances training efficiency and caption quality by leveraging rich sensor data. We create the Explicit Image Caption Reasoning Multimodal Model (ECRMM) by fine-tuning TinyLLaVA with the ICICD dataset. Experiments demonstrate ECR&rsquo;s effectiveness and robustness in processing sensor data, outperforming traditional methods