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ABSTRACT Objective: The aim was to characterize blood glucose fluctuations in patients with fulminant type 1 diabetes (FT1DM) at the stable stage using continuous blood glucose monitoring systems (CGMSs). Subjects and methods: Ten patients with FT1DM and 20 patients with classic type 1 diabetes mellitus (T1DM) (the control group) were monitored using CGMSs for 72 hours. Results: The CGMS data showed that the mean blood glucose (MBG), the standard deviation of the blood glucose (SDBG), the mean amplitude glycemic excursions (MAGE), the blood glucose areas and the percentages of blood glucose levels below 13.9 mmol/L were similar between the two groups. However, the percentage of blood glucose levels below 3.9 mmol/L was significantly higher in the FT1DM group compared to the T1DM group (p < 0.05). The minimum (Min) blood glucose level in the FT1DM group was significantly lower than that of the T1DM group (p < 0.05). Patients with FT1DM had severe dysfunction of the islet beta cells and alpha cells compared to patients with T1DM, as indicated by lower C-peptide values and higher glucagon/C-peptide values. Conclusion: In conclusion, patients with FT1DM at the stable stage were more prone to hypoglycemic episodes as recorded by CGMSs, and they had a greater association with severe dysfunction of both the beta and alpha islet cells compared to patients with T1DM.

This study examined the impact of current solution treatment on the microstructure and mechanical properties of the Co-28Cr-6Mo-0.22C alloy investment castings. The findings reveal that the current solution treatment significantly promotes the dissolution of carbides at a lower temperature. The optimal conditions for solution treatment are determined as a solution temperature of 1,125 °C and a holding time of 5.0 min. Under these parameters, the size and volume fraction of precipitated phases in the investment castings are measured as 6.2 µm and 1.1vol.%. The yield strength, ultimate tensile strength, and total elongation of the Co-28Cr-6Mo-0.22C investment castings are 535 MPa, 760 MPa, and 12.6%, respectively. These values exceed those obtained with the conventional solution treatment at 1,200 °C for 4.0 h. The findings suggest a phase transformation of M23C6s+C following the current solution treatment at 1,125 °C for 5.0 min. In comparison, the traditional solution treatment at 1,200 °C for 4.0 h leads to the formation of M23C6 and M6C carbides. It is noteworthy that the non-thermal effect of the current during the solution treatment modifies the free energy of both the matrix and precipitation phase. This modification lowers the phase transition temperature of the M23C6s+C reaction, thereby facilitating the dissolution of carbides. As a result, the current solution treatment approach achieves carbide dissolution at a lower temperature and within a significantly shorter time when compared to the traditional solution treatment methods. [ABSTRACT FROM AUTHOR]

Purpose: To analyse whether the stage of apical development affects the effectiveness of regenerative endodontic treatment by comparing the outcomes for necrotic mature and immature permanent teeth treated with regenerative endodontic procedures. Materials and Methods: Multiple databases (PubMed, Cochrane Library, Web of Science, EMBASE and OpenGrey databases) were searched through February 17th, 2022. Inclusion criteria were randomised controlled trials that included treatment of necrotic immature or mature permanent teeth using any regenerative endodontic procedures (REPs) that aimed to achieve pulp revascularisation or regeneration. The Cochrane Risk of Bias 2.0 tool was used to assess risk of bias. The included indicators were asymptomatic sign, success, pulp sensitivity, and discolouration. The extracted data were expressed by percentage for statistical analysis. The random effect model was used to explain the results. Comprehensive Meta-Analysis Version 2 was used to perform the statistical analyses. Results: Twenty-seven RCTs were eligible for inclusion in the meta-analysis. The success rates of necrotic immature and mature permanent teeth were 95.6% (95% CI, 92.4%-97.5%; I²=34.9%) and 95.5% (95%CI, 87.9%-98.4%; I²=0%), respectively. The asymptomatic rates of necrotic immature and mature permanent teeth were 96.2% (95%CI, 93.5%-97.9%; I²=30.1%) and 97.0% (95%CI, 92.6%-98.8%; I²=0%), respectively. The treatment of immature and mature necrotic permanent teeth with REPs yields high success rates and low symptomatic rates. The incidence of positive sensitivity response for electric pulp testing in necrotic immature permanent teeth (25.2% [95% CI, 18.2%-33.8%; I²=0%]) was lower than that in necrotic mature permanent teeth (45.4% [95% CI, 27.2%-64.8%; I²=75.2%]), and this difference was statistically significant. The restoration of pulp sensitivity seems to be more evident in necrotic mature permanent teeth than in necrotic immature permanent teeth. The crown discolouration rate of immature permanent teeth was 62.5% (95% CI, 49.7%-73.8%; I²=76.1%). Necrotic immature permanent teeth have a considerable crown discolouration rate. Conclusion: REPs for both immature and mature necrotic permanent teeth yield high success rates and promote root development. The vitality responses seem to be more evident in necrotic mature permanent teeth than in necrotic immature permanent teeth. [ABSTRACT FROM AUTHOR]

Background. Prediction of high-risk depression trajectories in the first year following breast cancer diagnosis with fMRI-related brain connectomics is unclear. Methods. The Be Resilient to Breast Cancer (BRBC) study is a multicenter trial in which 189/232 participants (81.5%) completed baseline resting-state functional magnetic resonance imaging (rs-fMRI) and four sequential assessments of depression (T0-T3). The latent growth mixture model (LGMM) was utilized to differentiate depression profiles (high vs. low risk) and was followed by multivoxel pattern analysis (MVPA) to recognize distinct brain connectivity patterns. The incremental value of brain connectomics in the prediction model was also estimated. Results. Four depression profiles were recognized and classified into high-risk (delayed and chronic, 14.8% and 12.7%) and low-risk (resilient and recovery, 50.3% and 22.2%). Frontal medial cortex and frontal pole were identified as two important brain areas against the high-risk profile outcome. The prediction model achieved 16.82-76.21% in NRI and 12.63-50.74% in IDI when brain connectomics were included. Conclusion. Brain connectomics can optimize the prediction against high-risk depression profiles in the first year since breast cancer diagnoses. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic value of metagenomic sequencing technology based on Illumina and Nanopore sequencing platforms for patients with suspected lower respiratory tract infection whose pathogen could not be identified by conventional microbiological tests. Methods: Patients admitted to the Respiratory and Critical Care Medicine in Shanghai Ruijin Hospital were retrospectively studied from August 2021 to March 2022. Alveolar lavage or sputum was retained in patients with clinically suspected lower respiratory tract infection who were negative in conventional tests. Bronchoalveolar lavage fluid (BALF) samples were obtained using bronchoscopy. Sputum samples were collected, while BALF samples were not available due to bronchoscopy contraindications. Samples collected from enrolled patients were simultaneously sent for metagenomic sequencing on both platforms. Results: Thirty-eight patients with suspected LRTI were enrolled in this study, consisting of 36 parts of alveolar lavage and 2 parts of sputum. According to the infection diagnosis, 31 patients were confirmed to be infected with pathogens, while 7 patients were diagnosed with non-infectious disease. With regard to the diagnosis of infectious diseases, the sensitivity and specificity of Illumina and Nanopore to diagnose infection in patients were 80.6% vs. 93.5% and 42.9 vs. 28.6%, respectively. In patients diagnosed with bacterial, Mycobacterium, and fungal infections, the positive rates of Illumina and Nanopore sequencer were 71.4% vs. 78.6%, 36.4% vs. 90.9%, and 50% vs. 62.5%, respectively. In terms of pathogen diagnosis, the sensitivity and specificity of pathogens detected by Illumina and Nanopore were 55.6% vs. 77.8% and 42.9% vs. 28.6%, respectively. Among the patients treated with antibiotics in the last 2 weeks, 61.1% (11/18) and 77.8% (14/18) cases of pathogens were accurately detected by Illumina and Nanopore, respectively, among which 8 cases were detected jointly. The consistency between Illumina and diagnosis was 63.9% (23/36), while the consistency between Nanopore and diagnosis was 83.3% (30/36). Between Illumina and Nanopore sequencing methods, the consistency ratio was 55% (22/ 42) based on pathogen diagnosis. Conclusion: Both platforms play a certain value in infection diagnosis and pathogen diagnosis of CMT-negative suspected LRTI patients, providing a theoretical basis for clinical accurate diagnosis and symptomatic treatment. The Nanopore platform demonstrated potential advantages in the identification of Mycobacterium and could further provide another powerful approach for patients with suspected Mycobacterium infection. [ABSTRACT FROM AUTHOR]

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering. Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction. However, most of the traditional deep learning models are less efficient to address generalization problems. To fill this technical gap, in this work, we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data. Specifically, the new model, named CGP-NN, consists of a novel parametric features extraction approach (1DCPP), a stacked multilayer gated recurrent model (multilayer GRU), and an adaptive physics-informed loss function. Through machine training, the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction. The CGP-NN model has the best generalization when the physicsrelated metric λ = 0:5. A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels. To validate the developed model and methodology, a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability. The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data. [ABSTRACT FROM AUTHOR]

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16 seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model. Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R² is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model. [ABSTRACT FROM AUTHOR]