Development and application of Chinese medical ontology for diabetes mellitus.

OBJECTIVE: To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. MATERIALS AND METHODS: We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. RESULTS: The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. CONCLUSION: CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

Dehydroabietane-type bifunctional organocatalysts in asymmetric synthesis: recent progress.

Dehydroabietane-type bifunctional organocatalysts derived from rosane-type diterpenes of dehydroabietic acid (DHAA) and dehydroabietylamine (DA) have been utilized in a wide variety of highly enantioselective reactions. Since one well-documented review exclusively reported on the development of terpene-derived bifunctional thioureas in asymmetric organocatalysis in 2013, fragmentary progress on the dehydroabietane-type bifunctional thioureas and squaramides has been mentioned in other reviews. In this mini-review, we systematically analyze and reorganize the published literature on dehydroabietane-type bifunctional organocatalysts in the recent decade according to the type of catalysts. Our aim is for this review to provide helpful research information and serve as a foundation for further design and application of rosin-based organocatalysts.

Eddy Current Measurement for Planar Structures.

Eddy current (EC) testing has become one of the most common techniques for measuring metallic planar structures in various industrial scenarios such as infrastructures, automotive, manufacturing, and chemical engineering. There has been significant progress in measuring the geometry, electromagnetic properties, and defects of metallic planar structures based on electromagnetic principles. In this review, we summarize recent developments in EC computational models, systems, algorithms, and measurement approaches for planar structures. First, the computational models including analytical models, numerical methods, and plate property estimation algorithms are introduced. Subsequently, the impedance measurement system and probes are presented. In plate measurements, sensor signals are sensitive to probe lift-off, and various algorithms for reducing the lift-off effect are reviewed. These approaches can be used for measureing thickness and electromagnetic properties. Furthermore, defect detection for metallic plates is also discussed.

Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records.

Objectives: Diabetes and its complications are commonly associated with depressive symptoms, and few studies have investigated the diagnosis effect of depressive symptoms in patients with diabetes. The present study used a network-based approach to explore the association between depressive symptoms, which are annotated from electronic health record (EHR) notes by a deep learning model, and the diagnosis of type 2 diabetes mellitus (T2DM) and its complications. Methods: In this study, we used anonymous admission notes of 52,139 inpatients diagnosed with T2DM at the first affiliated hospital of Nanjing Medical University from 2008 to 2016 as input for a symptom annotation model named T5-depression based on transformer architecture which helps to annotate depressive symptoms from present illness. We measured the performance of the model by using the F1 score and the area under the receiver operating characteristic curve (AUROC). We constructed networks of depressive symptoms to examine the connectivity of these networks in patients diagnosed with T2DM, including those with certain complications. Results: The T5-depression model achieved the best performance with an F1-score of 91.71 and an AUROC of 96.25 compared with the benchmark models. The connectivity of depressive symptoms in patients diagnosed with T2DM (p = 0.025) and hypertension (p = 0.013) showed a statistically significant increase 2 years after the diagnosis, which is consistent with the number of patients diagnosed with depression. Conclusion: The T5-depression model proposed in this study can effectively annotate depressive symptoms in EHR notes. The connectivity of annotated depressive symptoms is associated with the diagnosis of T2DM and hypertension. The changes in the network of depressive symptoms generated by the T5-depression model could be used as an indicator for screening depression.

Validation of type 2 diabetes subgroups by simple clinical parameters: a retrospective cohort study of NHANES data from 1999 to 2014.

OBJECTIVES: To verify whether a simplified method based on age, body mass index (BMI) and glycated haemoglobin (HbA1c) is feasible in classifying patients with type 2 diabetes (T2D), and evaluate the predictive ability of subgroups in several health and mortality outcomes. DESIGN: Retrospective cohort study. SETTING: The National Health and Nutrition Examination Survey 1999-2014 cycle. PARTICIPANTS: A total of 1960 participants with diabetes and the age at diagnosis greater than 30. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants with T2D were assigned to previously defined (by Ahlqvist) subgroups based on five variables: age, BMI, HbA1c, homoeostasis model assessment (HOMA) 2 estimates of ß-cell function (HOMA2-B), and insulin resistance (HOMA2-IR), and on three variables: age, BMI and HbA1c. The classification performances of the three variables were evaluated based on 10-fold cross validation, with accuracy, precision and recall as evaluation criteria. Outcomes were assessed using logistic regression and Cox regression analysis. RESULTS: Without HOMA measurements, it is difficult to identify severe insulin-resistant diabetes, but other subgroups can be ideally identified. There is no significant difference between the five variables and the three variables in the ability to predict the prevalence of poor cardiovascular health (CVH), chronic kidney disease, non-alcoholic fatty liver disease and advanced liver fibrosis, and the risk of all-cause, cardiovascular disease and cancer-related mortality (p>0.05), except the prevalence of poor CVH in mild age-related diabetes (p<0.05). CONCLUSIONS: A simple classification based on age, BMI and HbA1c could be used to identify T2D with several health and mortality risks, which is accessible in most individuals with T2D. Due to its simplicity and practicality, more patients with T2D can benefit from subgroup specific treatment paradigms.

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor.

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver-pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver-pickup, also termed as transmitter-receiver (T-R) sensor is designed with a small mean radius of 1 mm. In addition, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution, with a limit of 0.2 µm and selected as 0.25 mm. Experiments were conducted for imaging of an aluminium sheet with seven machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10% for tilt angles up to 60 degrees.

Design of dual targeting immunomicelles loaded with bufalin and study of their anti-tumor effect on liver cancer.

OBJECTIVE: Bufalin is an effective drug for the treatment of liver cancer. But its high toxicity, poor water-solubility, fast metabolism and short elimination half-life limit its use in tumor treatment. How to make the drug accumulate in the tumor and reduce side effects while maintaining its efficacy are urgent problems to be solved. The goal of this study is to solve these problems. METHODS: A copolymer with tunable poly-N-isopropylacrylamide and polylactic acid was designed and synthesized. The corresponding dual targeting immunomicelles (DTIs) loaded with bufalin (DTIs-BF) were synthesized by copolymer self-assembly in an aqueous solution. The size and structure of DTIs-BF were determined by ZetaSizer Nano-ZS and transmission electron microscopy. Then, its temperature sensitivity, serum stability, critical micelle concentration (CMC), entrapment efficiency (EE), drug release and non-cytotoxicity of blank block copolymer micelles (BCMs) were evaluated. Next, the effects of DTIs-BF on cellular uptake, cytotoxicity, and tumor cell inhibition were evaluated. Finally, the accumulation of DTIs-fluorescein isothiocyanate (FITC) and the in vivo anti-tumor effect were observed using an interactive video information system. RESULTS: DTIs-BF had a small size, spherical shape, good temperature sensitivity, high serum stability, low CMC, high EE, and slow drug release. The blank BCMs had very low cytotoxicity. Compared with free bufalin, the in vitro cellular internalization and cytotoxicity of DTIs-BF against SMMC-7721 cells were significantly enhanced, and the effects were obviously better at 40 °C than 37 °C. In addition, the therapeutic effect on SMMC-7721 cells was further enhanced by the programmed cell death specifically caused by bufalin. When DTIs-FITC were injected intravenously in BALB/c nude mice bearing liver cancer, the accumulation of FITC was significantly increased in tumors. CONCLUSION: DTIs-BF is a potentially effective nano-formulation and has broad prospects in the clinical treatment of liver cancer.

Reduction of Coil-Crack Angle Sensitivity Effect Using a Novel Flux Feature of ACFM Technique.

Alternating current field measurement (ACFM) testing is one of the promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach was proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the center of the excitation coil where the magnetic field is measured. It was found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack followed a sine wave shape. A customized accelerated finite element method solver programmed in MATLAB was adopted to simulate the performance of the designed sensor probe which could significantly improve the computation efficiency due to the small crack perturbation. The experiments were also carried out to validate the simulations. It was found that the ratio between the z and x components of the magnetic field remained stable under various rotation angles. It showed the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique.

A predictive model for respiratory distress in patients with COVID-19: a retrospective study.

BACKGROUND: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19. METHODS: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined. RESULTS: Neutrophil count >6.3×109/L, D-dimer level ≥1.00 mg/L, and temperature ≥37.3 °C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350×109/L, and platelet count of 125-350×109/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources. CONCLUSIONS: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively.

Petersen estimator, Chapman adjustment, list effects, and heterogeneity.

We use a nonparametric mixture model for the purpose of estimating the size of a population from multiple lists in which both the individual effects and list effects are allowed to vary. We propose a lower bound of the population size that admits an analytic expression. The lower bound can be estimated without the necessity of model-fitting. The asymptotical normality of the estimator is established. Both the estimator itself and that for the estimable bound of its variance are adjusted. These adjusted versions are shown to be unbiased in the limit. Simulation experiments are performed to assess the proposed approach and real applications are studied.