Sustained increases in antibiotic prescriptions per primary care consultation for upper respiratory tract infections in England during the COVID-19 pandemic.

Background: The responsible use of existing antimicrobials is essential in reducing the threat posed by antimicrobial resistance (AMR). With the introduction of restrictions during the COVID-19 pandemic, a substantial reduction in face-to-face appointments in general practice was observed. To understand if this shift in healthcare provision has impacted on prescribing practices, we investigated antibiotic prescribing for upper respiratory tract infections (URTI) consultations. Methods: We conducted an interrupted time-series analysis using patient-level primary care data to assess the impact of the COVID-19 pandemic on consultations and antibiotic prescribing for URTI in England. Results: We estimated an increase of 105.7 antibiotic items per 1000 URTI consultations (95% CI: 65.6-145.8; P < 0.001) after national lockdown measures in March 2020, with increases mostly sustained to May 2022. Conclusions: Overuse of antibiotics is known to be a driver of resistance and it is essential that efforts to reduce inappropriate prescribing continue subsequent to the COVID-19 pandemic. Further work should examine drivers of increased antibiotic prescribing for URTI to inform the development of targeted antibiotic stewardship interventions.

ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images.

BACKGROUND: Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects. METHODS: We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet. RESULTS: Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process. CONCLUSION: The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.

The motivations of Chinese firms in response to the Carbon Disclosure Project.

Through an analysis of data gathered from Chinese firms surveyed by the Carbon Disclosure Project (CDP), this paper studies the motivations of Chinese firms to respond to the CDP. The results indicate that (1) Chinese firms are more inclined to respond to the CDP survey for the sense-making motivation; (2) Chinese firms are less inclined to respond to the CDP survey due to the existence of proprietary costs for information disclosure; (3) self-interested political motivation is a negative motivation for Chinese firms to respond to the CDP survey; state-owned enterprises (SOEs) are less inclined to respond to the CDP survey than are non-SOEs; and (4) Chinese firms did not consider a financing motivation when deciding whether to respond to the CDP survey. However, the results of our further research show that if firms actively respond to the CDP survey, their financing constraints can be significantly reduced. This paper studies the four motivations for Chinese firms to respond to the CDP survey, contributing to the research of carbon emission disclosure. This paper highlights the importance of corporate carbon awareness for carbon emission disclosure, builds an understanding of the internal driving forces of response to the CDP survey among Chinese firms, and thus promotes the increase of Chinese corporate disclosure of carbon emission.