Ansteel (China)

This study aimed to investigate the relationship between anemia, stroke severity, short-term prognosis, and imaging features of infarction. The clinical features and imaging data of patients with anemia and acute ischemic stroke were retrospectively analyzed. All patients were divided into anemia and non-anemia groups, and according to the degree of anemia, they were divided into mild anemia group and moderate and severe anemia group. Stroke severity, short-term prognosis, and infarct lesion characteristics were compared between all groups. Among the 238 AIS patients, we found that the prevalence of anemia was 25.6%. There was significant correlation between the severity of anemia and NIHSS at admission (P = 0.01); The severity of anemia affected 90-day mRS (P = 0.01), and increased the length of hospital (P < 0.01). In the moderate and severe anemia group, there were 16 cases of watershed infarction, 15 of cerebral infarction, and 14 of corona radiata and centrum semiovale infarction. Multiple infarcts were more significantly common in the moderate and severe anemia group (P < 0.01). The degree of anemia correlated with the severity of acute ischemic stroke. Furthermore, the severity of anemia was closely related to patient prognosis. The infarct sites of moderate and severe anemia were mostly in the cerebral lobe, corona radiata, and centrum semiovale, and the watershed area was prone to infarction with multiple infarction lesions, which may be related to tissue hypoperfusion. By analyzing the clinical features and imaging of patients with anemia combined with acute ischemic stroke, we found: The anemia group had higher NIHSS at admission than the non-anemia group; The short-term prognosis of moderate and severe anemia group was worse than mild anemia group and non-anemia group; Imaging findings illustrated the infarct sites of the moderate and severe anemia group were mostly in the cerebral lobe, corona radiata, and centrum semiovale, and the watershed area was prone to infarction with multiple infarction lesions.

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value.

When patients report a penicillin allergy (self-reported penicillin allergy, SRPA), clinicians alter their antibiotic management of the patient. However, fewer than 5% of SRPA have life-threatening reactions, and receiving narrower spectrum antibiotics may result in under treatment. Reports from community studies suggest that patients with SRPA are more likely to come to harm, but as SRPA implies exposure to healthcare, it is unclear whether additional harm is as a result of co-morbidities and the need to receive healthcare, rather than a harm in itself. This raises the question of how great an impact SRPA has on hospital and ICU mortality for patients admitted to hospital. A literature search will be performed using MEDLINE, Embase and CINAHL (via OVID) in October 2024. The eligible studies will be of adult hospitalised patients with penicillin allergy compared with their non-allergic counterparts. We will extract demographic and outcome data including whether the patients were treated in ICU and mortality (28-days, in-ICU or in-hospital). If possible, the results will be pooled for a meta-analysis by combining the all-cause mortality of patients at 28 days, in-ICU or in-hospital. Heterogeneity will be assessed using the I2 or H2 statistics if the number of included studies is less than 10. Previous reports of poor outcomes from SRPA are often based around increased risk of increased antibiotic use and antibiotic resistance. The requirement of SRPA to develop where there is exposure to healthcare and co-morbidities may mean that these outcomes are derived from poorer health rather than from SRPA perse. The impact of SRPA on hospitalised patients is less understood, with outcomes that are well-defined, such as mortality. This review will outline the differences that exist, and a meta-analysis may define the size of any differences. PROSPERO CRD42024537658.