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Objective To investigate the development of SCIE and PubMed deep learning literature on chest X-ray imaging.Methods The literature on chest X-ray images published in SCIE and PubMed from January 1, 2017 to December 31, 2021 was searched, and the number of articles, publishing institutions, journals, citations, authors and keywords were statistically analyzed.Results A total of 440 papers were included, and the number of papers presented an annual growth trend. The country with the largest number of papers was the United States, with a total citation frequency of 4 409 times and an average citation frequency of 12.32 times. The IEEE Access in the United States published the most articles, reaching 29 articles. The number one publisher is Germany Springer Nature with 83 articles. There are 7 core authors, 10 of which have published the most papers, and the most frequently cited keywords in the research content are COVID-19.Conclusion The literature on deep learning in the field of chest X-ray imaging collected in SCIE and PubMed shows an overall upward trend year by year, mainly in English. However, a core author group has not yet been formed, and there is no clear leader with prolific citations and publications, and the number of high-impact publications is still limited.

Abstract Background Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. Methods Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. Results This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56–77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1–7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P

Strawberry quality grading helps producers to better manage inventory, transportation and sales, and improve product market competitiveness. Currently, this work is mostly carried out by manual grading, which is not only inefficient, but also relies on the experience of the grader, which is easy to cause grading errors. Aiming at the complex background information present in strawberry images, a grading fusion model is proposed, which adopts YOLOv5 target detection algorithm in the first level to recognize the strawberry in its natural state and crop its background information; In the second level, a stacking fusion model is adopted, which combines the neural network classification model and the machine learning classifier to realize the lossless quality grading of strawberry; The loss function is improved by introducing the Label Smoothing method, which makes the model more suitable for strawberry grading this kind of non-standard product classification task; Comparison experimental results show that the accuracy of the strawberry grading fusion model based on complex background images proposed in this paper reaches 85.4%, which improves the classification accuracy by 13% comparing with that of the single-stage model.

Background: Heart failure (HF) is the terminal stage of various heart diseases. Conventional treatments have poor efficacy, and diuretic resistance can present. Previous studies have found that the use of glucocorticoids can enhance the diuretic effect of patients with heart failure and reduce heart failure symptoms. However, the relationship between glucocorticoid use and mortality in patients with heart failure in intensive care units is unclear.Objectives: The aim of this study was to determine the association between glucocorticoid use and all-cause mortality in critically ill patients with heart failure. Methods: The information on patients with heart failure in this study was extracted from the MIMIC-III (Medical Information Mart for Intensive Care-III) database. Patients in the glucocorticoid and non-glucocorticoid groups were matched using propensity scores. The Kaplan-Meier method was used to explore the difference in survival probability between the two groups. A Cox proportional-hazards regression model was used to analyze the hazard ratios (HRs) for the two patient groups. Subgroup analyses were performed with prespecified stratification variables to demonstrate the robustness of the results.Results: The study included 9,482 patients: 2,099 in the glucocorticoid group and 7,383 in the non-glucocorticoid group. There were 2,055 patients in each group after propensity-score matching. The results indicated that the non-glucocorticoid group was not significantly associated with reduced mortality in patients with heart failure during the 14-day follow-up period [HRs = .901, 95% confidence interval (CI) = .767–1.059]. During the follow-up periods of 15–30 and 15–90 days, the mortality risk was significantly lower in the non-glucocorticoid group than in the glucocorticoid group (HRs = .497 and 95% CI = .370–.668, and HRs = .400 and 95% CI = .310–.517, respectively). Subgroup analyses indicated no interaction among each stratification variable and glucocorticoid use.Conclusion: Glucocorticoid use was associated with an increased mortality risk in critically ill patients with heart failure.

The quantum mechanical calculations of the conductance for ideal devices, having double-quantum-point constrictions (QPCs) connected in series by a cavity, are executed with the use of coupled mode transfer method and wave function matching technique. The quantum interference of the electron waves with different phases arising from multiple scattering and reflections of electrons in the cavity, are used in the determination of the characteristics of the conductance of the device.