The study aims to determine the effect of enhancing knowledge and awareness of the WHO Access, Watch, and Reserve (AWaRe) antibiotics classification on hospital clinical staff’s knowledge, attitudes and antibiotic prescribing practices. A pre-p...
Antimicrobial resistance (AMR) poses a global public health threat, with rates directly linked with consumption. The World Health Organization (WHO)’s AWaRe classification aims to guide antibiotic use, and is influenced by drug availability, af...
The WHO Essential Medicines List Access, Watch, and Reserve (AWaRe) classification could facilitate antibiotic stewardship and optimal use. In Bangladesh, data on antibiotic dispensing in pharmacies according to the AWaRe classification are scarce. W...
Irrational and inappropriate prescribing of antibiotics is a major problem that can lead to the development of antimicrobial resistance (AMR). In Zambia, there is insufficient information on the prescribing patterns of antibiotics according to the Wo...
Background: To aid in the development of antimicrobial stewardship programs (ASPs), we analyzed the patterns and trends in antibiotic prescriptions for patients with severe acute respiratory infection (SARI), utilizing the WHO’s AWaRe classific...
Background: The AWaRe tool was set up by the World Health Organization (WHO) to promote the rational use of antimicrobials. Indeed, this tool classifies antibiotics into four groups: access, watch, reserve and not-recommended antibiotics. In The Demo...
Globally, the inappropriate dispensing and use of antibiotics in animals has contributed to the development of bacterial antimicrobial resistance (AMR). In Zambia, there is insufficient information among community pharmacy professionals on antibiotic...
The use of antibiotics is the most important modifiable risk factor for the development of microorganism resistance. A cross-sectional study of outpatients receiving antibiotic prescriptions registered in a population database in Colombia was conduct...
Background: Monitoring antibiotic consumption represents an important objective for antimicrobial stewardship programs in hospitals. The aim of this study was to analyze antibiotic consumption patterns in three Romanian hospitals. Methods: A five-yea...
Background/Objectives: The present study aimed to analyze systemic antibiotic prescription patterns in the Salerno Local Health Authority (ASL Salerno) during 2024. The objective was to describe territorial antibiotic consumption and monitor prescrib...
Objectives: Outpatient antibiotic prescribing is a major driver of antimicrobial resistance, yet detailed long-term analyses of prescribing patterns in Croatia remain limited. This study aimed to analyze trends in outpatient antibiotic prescriptions...
Background: Antibiotic resistance is one of the most pressing public health problems. Health authorities, patients, and health professionals, including dentists, are all involved in its development. COVID-19 pandemic restrictions on dental care may h...
The purpose of this study is to conduct a comparative analysis of the consumption of antibiotics for systemic use reimbursed by the state in Kazakhstan for 2017–2019 with the Access, Watch, and Reserve classification (AWaRe 2019) of the World H...
This pilot study was conducted to analyze the quality of the antimicrobials sold in the street markets in Port-au-Prince, Haiti. A total of 258 packs containing antimicrobials were bought in 28 street markets in Port-au-Prince (Haiti). Tablets and co...
The COVID-19 pandemic affected the epidemiology of infectious diseases and changed the operation of health care systems and health care seeking behavior. Our study aimed to analyze the utilization of systemic antibiotics in ambulatory care in Hungary...
Background and Objectives: Healthcare-associated infections (HAIs) remain a major challenge in emergency hospital settings, where high patient turnover and empirical antibiotic use may contribute to the emergence and spread of multidrug-resistant org...
The containment measures following COVID-19 pandemic drastically reduced airway infections, but they also limited the access of patients to healthcare services. We aimed to assess the antibiotic prescription patterns in the Italian paediatric primary...
22 October 2025
Empirical antibiotic treatment is common in acute conjunctivitis despite most cases being non-infectious or viral infections. Operational research (OR) at an eye hospital in Ghana (January–December 2021) identified appropriate antibiotic prescr...
The COVID-19 pandemic shifted pediatric infection epidemiology from bacterial toward viral predominance as public health measures relaxed. To determine how hospital antibiotic prescribing adapted, antimicrobial consumption was characterized and linke...
Coronavirus disease 2019 (COVID-19) has overlapping clinical characteristics with bacterial respiratory tract infection, leading to the prescription of potentially unnecessary antibiotics. This study aimed at measuring changes and patterns of nationa...
Monitoring antibiotic consumption is crucial to tackling antimicrobial resistance. However, currently there is no system in Sierra Leone for recording and reporting on antibiotic consumption. We therefore conducted a cross-sectional study to assess n...
The aim of this study was to determine and describe trends in antibiotics utilization in Serbia over a ten-year period. Data were retrieved from publicly available annual reports (2010–2019). The results were expressed as Defined Daily Dose (DDD) per...
Antimicrobial resistance (AMR) compromises global health due to the associated morbidity, mortality, and costs. The inappropriate use of antimicrobial agents is a prime driver of AMR. Consequently, it is imperative to gain a greater understanding of...
Background: Antimicrobial resistance (AMR) is a public health concern worldwide, particularly in low-to-middle-income countries with few antimicrobial stewardship programs and few laboratories equipped for diagnosis. Methods: As point-prevalence surv...
Background/Objectives: Improper use of systemic antibiotics remains a significant concern in hospital settings, contributing to increased antimicrobial resistance and suboptimal clinical outcomes. The COVID-19 pandemic exacerbated this issue. This st...
Background: Antimicrobial resistance (AMR) poses a critical global public health challenge requiring comprehensive intervention strategies, including robust antibiotic stewardship programs. The European Commission’s 2017 One Health Action Plan...
Background and Objective: Irrational use and overuse of antibiotics is considered a major cause of antimicrobial resistance (AMR) among patients admitted to hospitals, especially in intensive care units (ICUs). ICUs are the most critical wards in hea...
Background/Objectives: Surveillance of local antibiotic resistance plays a critical role in guiding empirical antibiotic therapies in emergency departments (EDs). This study aimed to collect and analyze a five-year period of prevalence and AMR rates...
For supporting antibiotic stewardship interventions, the World Health Organization (WHO) classified antibiotics through the AWaRe (Access, Watch, and Reserve) classification. Inappropriate use of antimicrobials among hospital-admitted patients expose...
Background and objectives: There are concerns with the current prescribing practices of antibiotics in ambulatory care in Tanzania, including both the public and private sectors. These concerns need to be addressed as part of the national action plan...
Deep learning is now receiving widespread attention in hyperspectral image (HSI) classification. However, due to the imbalance between a huge number of weights and limited training samples, many problems and difficulties have arisen from the use of d...
Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integra...
The ageing of the population—especially in developed countries—has brought on many societal challenges and has significantly contributed to the burden on healthcare infrastructures worldwide. Elderly persons (aged ≥ 65 years) are at higher risk for d...
Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral...
We conducted a point prevalence survey (PPS) to determine the prevalence of antibiotic use at Geita Regional Referral Hospital (GRRH) located along the shores of Lake Victoria in north-western Tanzania. This has led to the identification of gaps for...
Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potentia...
Text classification is an important research field in text mining and natural language processing, gaining momentum with the growth of social networks. Despite the accuracy advancements made by deep learning models, existing graph neural network-base...
Semantic feature learning on 3D point clouds is quite challenging because of their irregular and unordered data structure. In this paper, we propose a novel structure-aware convolution (SAC) to generalize deep learning on regular grids to irregular 3...
Accurate surface defect classification is a critical requirement in structural health monitoring and infrastructure inspection, where defects, including cracks, spalling, delamination and noncrack regions, often appear with low-contrast and complex b...
Accurate pest identification plays a critical role in effective pest management and sustainable agricultural production. However, fine-grained pest classification is inherently challenging due to low inter-class separability, significant intra-class...
Dictionary learning has been an important role in the success of data representation. As a complete view of data representation, hybrid dictionary learning (HDL) is still in its infant stage. In previous HDL approaches, the scheme of how to learn an...
Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose...
With the widespread use of over-the-top (OTT) media, such as YouTube and Netflix, network markets are changing and innovating rapidly, making it essential for network providers to quickly and efficiently analyze OTT traffic with respect to pricing pl...
Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubt...
Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and...
Background: Deep learning approaches, especially convolutional neural networks (CNNs) and Vision Transformers (ViTs), have made substantial progress in breast cancer classification from histopathological images. Yet, previous CNN–ViT-based arch...
Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often...
Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings. First,...
The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identifica...
Sentiment classification often relies on costly in-domain annotations, while weak-supervision sources provide low-cost but inconsistent labels. We propose Multi-Source Reliability-Aware Aggregation and Cross-domain Consistency Learning (MSRA-CCL) for...
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