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To analyze the influence of diabetes mellitus on the clinical outcomes of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP). Methods: This retrospective study included patients diagnosed with MSAP and SAP at Shanxi Bethune Hospital from January 1, 2017, to December 31, 2021. Clinical data were collected, including patient demographics, 24-hour laboratory indicators, and inflammation indices. Propensity score matching (PSM) was used to compare outcomes before and after matching. Patients were randomized into training and validation sets (7:3) to develop and validate a clinical prediction model for infected pancreatic necrosis (IPN). Results: Among 421 patients, 79 had diabetes at admission. Before PSM, diabetic patients had higher incidences of peripancreatic fluid (71% vs 47%, p< 0.001) and IPN (48% vs 10%, p< 0.001), higher surgical intervention rates (24% vs 12%, p=0.008), and significant differences in abdominocentesis (22% vs 11%, p=0.014). After PSM, 174 patients were matched, and the diabetes group still showed higher incidences of peripancreatic fluid (69% vs 47%, p=0.008), IPN (48% vs 11%, p< 0.001), and surgical intervention rates (27% vs 13%, p=0.037). Diabetes, modified CT severity index (MCTSI), serum calcium, and HDL-c were identified as independent risk factors for IPN. The prediction model demonstrated good predictive value. Conclusion: In MSAP and SAP patients, diabetes mellitus can exert an influence on their clinical outcome and is an independent risk factor for IPN. The alignment diagram and web calculator constructed on the basis of diabetes mellitus, modified CT severity index (MCTSI), serum calcium and high-density lipoprotein cholesterol (HDL-c) have good predictive value and clinical guidance for the occurrence of IPN in MSAP and SAP. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the most common malignant tumors in the world, and its incidence and mortality are increasing year by year. The prognosis of liver cancer depends on the stage of liver cancer, the treatment method, the liver function, and individual differences. The prognosis of liver cancer mainly worsens with the progression of the stage. The prediction and staging system of liver cancer prognosis plays a very important role in the outcome of liver cancer prognosis, providing some guidance for clinical practice and bringing benefits for patients. This article reports on the prediction models and staging systems that have been applied in the field of liver cancer in the past 5 years, objectively analyzes the advantages and disadvantages, applicable population of each model and staging system, and searches for other patient and clinical characteristics that need to be considered for successfully establishing a prediction model, aiming to improve the specificity, sensitivity, and accuracy of liver cancer prediction and increase the overall survival rate of liver cancer. [ABSTRACT FROM AUTHOR]

We introduce a new family of probability distributions that we call the Modified-Half-Normal distributions. It is supported on the positive part of the real line with its probability density proportional to the function x ↦ x α − 1 exp (− β x 2 + γ x) I (x > 0) , α > 0 , β > 0 , γ ∈ R. We explore a number of its properties including showing the fact that the normalizing constant and moments of the distribution can be represented in terms of the Fox-Wright function. We demonstrate its relevance by showing its connection to a number of Bayesian statistical methods appearing from multiple areas of research such as Bayesian Binary regression, Analysis of Directional data, and Bayesian graphical model. The availability of its efficient sampling scheme is important to the success of such Bayesian procedures. Therefore, a major focus of this article is the development of methods for generating random samples from the Modified-Half-Normal distribution. To ensure efficiency, we prove that the constructed accept reject algorithms are "uniformly efficient" with high acceptance probability irrespective of the choice of the parameter specifications. Finally, we provide basic inference procedure for its parameters when analyzing data assuming the Modified-Half-Normal probability model. [ABSTRACT FROM AUTHOR]

To solve the unreasonable energy distribution and excessive energy waste in the steam system of iron and steel (I&S) industry, a mixed integer linear programming model based on the theory of exergy and energy level is developed in this study to optimize the steam system. After optimization, the utilization potential of waste energy is found. Then, several waste energy recovery technologies are compared according to their application results. The results show that the exergy efficiency of steam system is 74.065%. After optimization, the exergy efficiency of steam system is increased by 10.568%, the energy input of the I&S enterprise is reduced by 84.30 MJ t−1‐CS−1, and waste energy potential is found at hot rolling, sintering, and converter. Herein, flue gas waste heat from hot rolling and sintering is recovered using series two‐stage Organic Rankine Cycle with cyclopentane as the working fluid, and steam waste energy from converter is recovered using saturated steam power generation. These waste energy technologies increase the electricity generation of I&S enterprise by 15.25 kWh t−1‐CS−1. Overall, combining the optimization model with the use of waste energy recovery technologies is necessary and effective for I&S enterprise to increase their energy efficiency. [ABSTRACT FROM AUTHOR]

To demonstrate the effect of body posture on the occupant dynamic response under a spacecraft high-level re-entry acceleration overload, seat-dummy system models with typical body positions, including supine and lying positions, are established in this study using Pro-E and HyperWorks software. A Human Model for Safety (HUMOS version II) dummy was used in the model. Seat-dummy system models were calibrated and validated according to actual centrifugal test data. The effect of body posture on the overload response of key body organs, including the heart, lung, diaphragm, liver, and abdominal viscera are demonstrated and analyzed under a high-level acceleration overload (a peak value of 6.4 g as input). Simulation results show that the displacement and deformation of the diaphragm are the most important factors affecting human tolerance to re-entry overload. The acting force of the diaphragm from other organs' inertial reaction in supine position is larger than that in the lying position. Therefore, spacecraft designers should focus more on the mechanical status of human internal organs and provide a body posture for better protection of the crew during spacecraft re-entry. [ABSTRACT FROM AUTHOR]

Allisartan isoproxil (AI) is a blocker of the angiotensin II type 1 receptor. We evaluated the safety and pharmacokinetics of single‐ and multiple‐dose AI in healthy Chinese individuals. Participants were assigned to receive AI or placebo. Plasma concentration of EXP3174 (carboxylic acid derivative) was measured using liquid chromatography‐tandem mass spectrometry. Pharmacokinetic parameters were determined by noncompartmental methods. Twelve subjects were enrolled, and the ratio of men to women was 5:1. Main pharmacokinetic parameters of EXP3174 after single and multiple doses of AI were a mean maximum concentration in plasma (Cmax) of 2242 ± 1037 ng/mL and median time to reach Cmax (Tmax) of 3.5 hours (2.5‐8 hours). The median Tmax, at steady state was 4.0 hours (1.5‐8 hours). The mean Cmax at steady state (Cmax, SS) was 2047 ± 1050 ng/mL. In terms of EXP3174, there was no significant difference in the Cmax, SS, area under the curve from time zero to 24 hours of quantifiable concentration at steady state (AUC0–24 SS), and AUC0‐72 after multiple doses of AI. Serious adverse events did not occur. These data suggest that AI is safe and well tolerated in healthy Chinese individuals at a single dose of 480 or 480 mg once daily for 7 days. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the most serious brain diseases, which often result in a short life. However, in developing areas, medical resources are in shortage, which affect the diagnosis of brain tumors. With the development of computer science, many diseases can be diagnosed by telemedicine systems, which help physicians save much time and improve diagnostic accuracy. Therefore, we propose a semantic segmentation method for brain tumors based on nested residual attention networks. It can be deployed in social mx'edia environment to work as a remote diagnosis system. The proposed method uses an improved residual attention block (RAB) as the basic unit. Based on the improved RAB, a nested RAB is designed to build the proposed method, which has better generalization. The proposed method includes an encoder part, a decoder part and skip connections. The encoder part learns multiple feature representations from inputs and the decoder part utilizes the learnt features to make segmentation predictions. In addition, high-level attention feature maps are exploited to induce low-level feature maps in skip connections to discard useless information. The proposed method is mainly validated on the dataset of Brain Tumor Segmentation challenge (BraTS) 2015. The proposed method achieves an average dice score of 0.87 (0.80, 0.72) for the whole tumor (core tumor, enhancing tumor) regions on BraTS 2015 dataset. [ABSTRACT FROM AUTHOR]