Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%

An AI-Assisted Skincare Routine Recommendation System in XR

In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines

End-to-end deep graph convolutional neural network approach for intentional islanding in power systems considering load-generation balance

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result

Global antibiotic dosing strategies in hospitalised children: Characterising variation and implications for harmonisation of international guidelines

Background Paediatric global antibiotic guidelines are inconsistent, most likely due to the limited pharmacokinetic and efficacy data in this population. We investigated factors underlying variation in antibiotic dosing using data from five global point prevalence surveys. Methods & findings Data from 3,367 doses of the 16 most frequent intravenous antibiotics administered to children 1 month–12 years across 23 countries were analysed. For each antibiotic, we identified standard doses given as either weight-based doses (in mg/kg/day) or fixed daily doses (in mg/day), and investigated the pattern of dosing using each strategy. Factors underlying observed variation in weight-based doses were investigated using linear mixed effects models. Weight-based dosing (in mg/kg/day) clustered around a small number of peaks, and all antibiotics had 1–3 standard weight-based doses used in 5%-48% of doses. Dosing strategy was more often weight-based than fixed daily dosing for all antibiotics apart from teicoplanin, which had approximately equal proportions of dosing attributable to each strategy. No strong consistent patterns emerged to explain the historical variation in actual weight-based doses used apart from higher dosing seen in central nervous system infections, and lower in skin and soft tissue infections compared to lower respiratory tract infections. Higher dosing was noted in the Americas compared to the European region. Conclusions Antibiotic dosing in children clusters around a small number of doses, although variation remains. There is a clear opportunity for the clinical, scientific and public health communities to consolidate behind a consistent set of global antibiotic dosing guidelines to harmonise current practice and prioritise future research

New approaches in the diagnosis and treatment of latent tuberculosis infection

With nearly 9 million new active disease cases and 2 million deaths occurring worldwide every year, tuberculosis continues to remain a major public health problem. Exposure to Mycobacterium tuberculosis leads to active disease in only ~10% people. An effective immune response in remaining individuals stops M. tuberculosis multiplication. However, the pathogen is completely eradicated in ~10% people while others only succeed in containment of infection as some bacilli escape killing and remain in non-replicating (dormant) state (latent tuberculosis infection) in old lesions. The dormant bacilli can resuscitate and cause active disease if a disruption of immune response occurs. Nearly one-third of world population is latently infected with M. tuberculosis and 5%-10% of infected individuals will develop active disease during their life time. However, the risk of developing active disease is greatly increased (5%-15% every year and ~50% over lifetime) by human immunodeficiency virus-coinfection. While active transmission is a significant contributor of active disease cases in high tuberculosis burden countries, most active disease cases in low tuberculosis incidence countries arise from this pool of latently infected individuals. A positive tuberculin skin test or a more recent and specific interferon-gamma release assay in a person without overt signs of active disease indicates latent tuberculosis infection. Two commercial interferon-gamma release assays, QFT-G-IT and T-SPOT.TB have been developed. The standard treatment for latent tuberculosis infection is daily therapy with isoniazid for nine months. Other options include therapy with rifampicin for 4 months or isoniazid + rifampicin for 3 months or rifampicin + pyrazinamide for 2 months or isoniazid + rifapentine for 3 months. Identification of latently infected individuals and their treatment has lowered tuberculosis incidence in rich, advanced countries. Similar approaches also hold great promise for other countries with low-intermediate rates of tuberculosis incidence

Use of the WHO Access, Watch, and Reserve classification to define patterns of hospital antibiotic use (AWaRe): an analysis of paediatric survey data from 56 countries

BACKGROUND: Improving the quality of hospital antibiotic use is a major goal of WHO's global action plan to combat antimicrobial resistance. The WHO Essential Medicines List Access, Watch, and Reserve (AWaRe) classification could facilitate simple stewardship interventions that are widely applicable globally. We aimed to present data on patterns of paediatric AWaRe antibiotic use that could be used for local and national stewardship interventions. METHODS: 1-day point prevalence survey antibiotic prescription data were combined from two independent global networks: the Global Antimicrobial Resistance, Prescribing, and Efficacy in Neonates and Children and the Global Point Prevalence Survey on Antimicrobial Consumption and Resistance networks. We included hospital inpatients aged younger than 19 years receiving at least one antibiotic on the day of the survey. The WHO AWaRe classification was used to describe overall antibiotic use as assessed by the variation between use of Access, Watch, and Reserve antibiotics, for neonates and children and for the commonest clinical indications. FINDINGS: Of the 23 572 patients included from 56 countries, 18 305 were children (77·7%) and 5267 were neonates (22·3%). Access antibiotic use in children ranged from 7·8% (China) to 61·2% (Slovenia) of all antibiotic prescriptions. The use of Watch antibiotics in children was highest in Iran (77·3%) and lowest in Finland (23·0%). In neonates, Access antibiotic use was highest in Singapore (100·0%) and lowest in China (24·2%). Reserve antibiotic use was low in all countries. Major differences in clinical syndrome-specific patterns of AWaRe antibiotic use in lower respiratory tract infection and neonatal sepsis were observed between WHO regions and countries. INTERPRETATION: There is substantial global variation in the proportion of AWaRe antibiotics used in hospitalised neonates and children. The AWaRe classification could potentially be used as a simple traffic light metric of appropriate antibiotic use. Future efforts should focus on developing and evaluating paediatric antibiotic stewardship programmes on the basis of the AWaRe index. FUNDING: GARPEC was funded by the PENTA Foundation. GARPEC-China data collection was funded by the Sanming Project of Medicine in Shenzhen (SZSM2015120330). bioMérieux provided unrestricted funding support for the Global-PPS

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Comparing neonatal and paediatric antibiotic prescribing between hospitals: A new algorithm to help international benchmarking

Objectives: The WHO anatomical therapeutic chemical (ATC)/defined daily dose (DDD) methodology is a standardized method of comparing antimicrobial use. The ATC/DDD is defined as the average maintenance daily dose of a drug used in a 70 kg adult, ignoring the considerable differences in body weight of neonates and children. The aim of this study was to develop a new standardized way of comparing rates of antimicrobial prescribing between European children's hospitals. Methods: This pilot study at four European children's hospitals (in the UK, Greece and Italy) collected data including demographics, antibiotic use, dosing and indication in children and neonates over a 14 day period. Results: A total of 1217 antibiotic prescriptions were issued with 47 different antibiotics used. Approximately half of all children and a third of all neonates received antibiotics, with wide variation between centres in the type and dose of antibiotic used. We propose a new pragmatic three-step algorithm. The first step includes a simple comparison of the proportion of hospitalized children on antibiotics by weight bands and the number of antimicrobials that account for 90% of total DDD drug usage (DU90%). The second step is a comparison of the dosing used (mg/kg/day). The third step is to compare overall drug exposure using DDD/100 bed days for standardized weight bands between centres. Conclusions: This novel method has the potential to be a useful tool to provide antibiotic use comparator data and requires validation in a large prospective point prevalence study. © The Author 2012. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved