Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Improving Autonomous Vehicles Maneuverability and Collision Avoidance in Adverse Weather Conditions Using Generative Adversarial Networks

In recent years, there has been a significant increase in the development of autonomous vehicles. One critical task for ensuring their safety and dependability, is obstacle avoidance in challenging weather conditions. However, no studies have explored the use of data augmentation to generate training data for Deep learning (DL) models aimed at navigating obstacles in extreme weather conditions. This study makes a substantial contribution to the field of autonomous vehicle obstacle avoidance by introducing an innovative approach that utilizes a Generative Adversarial Network (GAN) model for data augmentation, with the objective of enhancing the accuracy of DL models. The use of a GAN model to generate a training dataset and integrate images depicting challenging weather conditions has been pivotal in enhancing the accuracy of the DL models. The extensive training dataset, consisting of 64,336 images, was created using three cameras installed in VSim-AV, an autonomous vehicle simulator, thereby ensuring a diverse and comprehensive dataset for training purposes. Three DL models (ResNet50, ResNet101, and VGG16 transfer learning) were trained on this dataset both before and after applying the data augmentation techniques. The performance of the augmented models was evaluated in a real-time environment using the autonomous mode of the VSim-AV simulator. The testing phase resulted in the highest accuracy rate of 97.2% when employing Resnet101 following the implementation of GAN. It was observed that the autonomous car could navigate without any collisions, showcasing a remarkable reaction time of 0.105 seconds, thus affirming the effectiveness of the approach. The comparison between the original and augmented datasets demonstrate the originality and value of this study, showcasing its significant contribution to the advancement of autonomous vehicle obstacle avoidance technology. This paper makes significant advances to the field of autonomous vehicle navigation by exploiting Generative Adversarial Networks (GANs) to improve obstacle avoidance capabilities in severe weather conditions, hence increasing safety and dependability in real-world applications

Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces

Tunisian Multicenter Study on the Prevalence of Colistin Resistance in Clinical Isolates of Gram Negative Bacilli: Emergence of <i>Escherichia coli</i> Harbouring the <i>mcr-1</i> Gene

Background: Actually, no data on the prevalence of plasmid colistin resistance in Tunisia are available among clinical bacteria. Objectives: This study aimed to investigate the current epidemiology of colistin resistance and the spread of the mcr gene in clinical Gram-negative bacteria (GNB) isolated from six Tunisian university hospitals. Methods: A total of 836 GNB strains were inoculated on COL-R agar plates with selective screening agar for the isolation of GNB resistant to colistin. For the selected isolates, mcr genes, beta-lactamases associated-resistance genes and molecular characterisation were screened by PCRs and sequencing. Results: Colistin-resistance was detected in 5.02% (42/836) of the isolates and colistin-resistant isolates harboured an ESBL (blaCTX-M-15) and/or a carbapenemase (blaOXA-48, blaVIM) encoding gene in 45.2% of the cases. The mcr-1 gene was detected in four E. coli isolates (0.59%) causing urinary tract infections and all these isolates also contained the blaTEM-1 gene. The blaCTX-M-15 gene was detected in three isolates that also carried the IncY and IncFIB replicons. The genetic environment surrounding the mcr-carrying plasmid indicated the presence of pap-2 gene upstream mcr-1 resistance marker with unusual missing of ISApl1 insertion sequence. The Conclusions: This study reports the first description of the mcr-1 gene among clinical E. coli isolates in Tunisia and provides an incentive to conduct routine colistin susceptibility testing in GNB clinical isolates

Bacterial Pathogens and Antibiotic Resistance in Bloodstream Infections in Tunisia: A 13-Year Trend Analysis

The antimicrobial resistance (AMR) surveillance network has been monitoring bloodstream bacterial pathogens and their resistance since 1999 in Tunisia. We report the long-term trends in the distribution of bloodstream bacterial pathogens and their resistance patterns from this surveillance database. We analyzed antibiotic resistance rates in 11 tertiary teaching hospital laboratories under the AMR surveillance network during 2011&ndash;2023, focusing on six priority bacterial pathogens, using the Cochrane&ndash;Armitage test for trend analysis. Of 22,795 isolates, K. pneumoniae (38.5%) was the most common, followed by S. aureus (20.4%), E. coli (13.6%), and A. baumannii (10.3%). Carbapenem resistance was highest in A. baumannii (77%), followed by Pseudomonas aeruginosa (29.3%), K. pneumoniae (19.4%), and Enterobacter cloacae (6.8%). Carbapenem-resistant Enterobacterales and third-generation cephalosporin-resistant Enterobacterales (3GCREB) increased from 10.6% to 26.3% (p-value &lt; 0.001), and from 39% to 50.2%, respectively, during 2011&ndash;2023 (p-value &lt; 0.001). Vancomycin resistance (38.3%) and the emergence of linezolid resistance in 2019 (2.4%) were reported in E. faecium isolates. Resistance to carbapenems and 3GC is a major challenge to controlling BSI in Tunisia. The national AMR surveillance network helps monitor annual patterns and guides empirical therapy. An integrated database combining clinical profiles and resistance data via real-time data-sharing platforms could improve clinical decision-making

The Delta variant wave in Tunisia: Genetic diversity, spatio-temporal distribution and evidence of the spread of a divergent AY.122 sub-lineage

IntroductionThe Delta variant posed an increased risk to global public health and rapidly replaced the pre-existent variants worldwide. In this study, the genetic diversity and the spatio-temporal dynamics of 662 SARS-CoV2 genomes obtained during the Delta wave across Tunisia were investigated.MethodsViral whole genome and partial S-segment sequencing was performed using Illumina and Sanger platforms, respectively and lineage assignemnt was assessed using Pangolin version 1.2.4 and scorpio version 3.4.X. Phylogenetic and phylogeographic analyses were achieved using IQ-Tree and Beast programs.ResultsThe age distribution of the infected cases showed a large peak between 25 to 50 years. Twelve Delta sub-lineages were detected nation-wide with AY.122 being the predominant variant representing 94.6% of sequences. AY.122 sequences were highly related and shared the amino-acid change ORF1a:A498V, the synonymous mutations 2746T&amp;gt;C, 3037C&amp;gt;T, 8986C&amp;gt;T, 11332A&amp;gt;G in ORF1a and 23683C&amp;gt;T in the S gene with respect to the Wuhan reference genome (NC_045512.2). Spatio-temporal analysis indicates that the larger cities of Nabeul, Tunis and Kairouan constituted epicenters for the AY.122 sub-lineage and subsequent dispersion to the rest of the country.DiscussionThis study adds more knowledge about the Delta variant and sub-variants distribution worldwide by documenting genomic and epidemiological data from Tunisia, a North African region. Such results may be helpful to the understanding of future COVID-19 waves and variants.</jats:sec

Data_Sheet_3_The Delta variant wave in Tunisia: Genetic diversity, spatio-temporal distribution and evidence of the spread of a divergent AY.122 sub-lineage.PDF

IntroductionThe Delta variant posed an increased risk to global public health and rapidly replaced the pre-existent variants worldwide. In this study, the genetic diversity and the spatio-temporal dynamics of 662 SARS-CoV2 genomes obtained during the Delta wave across Tunisia were investigated.MethodsViral whole genome and partial S-segment sequencing was performed using Illumina and Sanger platforms, respectively and lineage assignemnt was assessed using Pangolin version 1.2.4 and scorpio version 3.4.X. Phylogenetic and phylogeographic analyses were achieved using IQ-Tree and Beast programs.ResultsThe age distribution of the infected cases showed a large peak between 25 to 50 years. Twelve Delta sub-lineages were detected nation-wide with AY.122 being the predominant variant representing 94.6% of sequences. AY.122 sequences were highly related and shared the amino-acid change ORF1a:A498V, the synonymous mutations 2746T>C, 3037C>T, 8986C>T, 11332A>G in ORF1a and 23683C>T in the S gene with respect to the Wuhan reference genome (NC_045512.2). Spatio-temporal analysis indicates that the larger cities of Nabeul, Tunis and Kairouan constituted epicenters for the AY.122 sub-lineage and subsequent dispersion to the rest of the country.DiscussionThis study adds more knowledge about the Delta variant and sub-variants distribution worldwide by documenting genomic and epidemiological data from Tunisia, a North African region. Such results may be helpful to the understanding of future COVID-19 waves and variants.</p

Data_Sheet_4_The Delta variant wave in Tunisia: Genetic diversity, spatio-temporal distribution and evidence of the spread of a divergent AY.122 sub-lineage.PDF

IntroductionThe Delta variant posed an increased risk to global public health and rapidly replaced the pre-existent variants worldwide. In this study, the genetic diversity and the spatio-temporal dynamics of 662 SARS-CoV2 genomes obtained during the Delta wave across Tunisia were investigated.MethodsViral whole genome and partial S-segment sequencing was performed using Illumina and Sanger platforms, respectively and lineage assignemnt was assessed using Pangolin version 1.2.4 and scorpio version 3.4.X. Phylogenetic and phylogeographic analyses were achieved using IQ-Tree and Beast programs.ResultsThe age distribution of the infected cases showed a large peak between 25 to 50 years. Twelve Delta sub-lineages were detected nation-wide with AY.122 being the predominant variant representing 94.6% of sequences. AY.122 sequences were highly related and shared the amino-acid change ORF1a:A498V, the synonymous mutations 2746T>C, 3037C>T, 8986C>T, 11332A>G in ORF1a and 23683C>T in the S gene with respect to the Wuhan reference genome (NC_045512.2). Spatio-temporal analysis indicates that the larger cities of Nabeul, Tunis and Kairouan constituted epicenters for the AY.122 sub-lineage and subsequent dispersion to the rest of the country.DiscussionThis study adds more knowledge about the Delta variant and sub-variants distribution worldwide by documenting genomic and epidemiological data from Tunisia, a North African region. Such results may be helpful to the understanding of future COVID-19 waves and variants.</p

Data_Sheet_2_The Delta variant wave in Tunisia: Genetic diversity, spatio-temporal distribution and evidence of the spread of a divergent AY.122 sub-lineage.PDF

IntroductionThe Delta variant posed an increased risk to global public health and rapidly replaced the pre-existent variants worldwide. In this study, the genetic diversity and the spatio-temporal dynamics of 662 SARS-CoV2 genomes obtained during the Delta wave across Tunisia were investigated.MethodsViral whole genome and partial S-segment sequencing was performed using Illumina and Sanger platforms, respectively and lineage assignemnt was assessed using Pangolin version 1.2.4 and scorpio version 3.4.X. Phylogenetic and phylogeographic analyses were achieved using IQ-Tree and Beast programs.ResultsThe age distribution of the infected cases showed a large peak between 25 to 50 years. Twelve Delta sub-lineages were detected nation-wide with AY.122 being the predominant variant representing 94.6% of sequences. AY.122 sequences were highly related and shared the amino-acid change ORF1a:A498V, the synonymous mutations 2746T>C, 3037C>T, 8986C>T, 11332A>G in ORF1a and 23683C>T in the S gene with respect to the Wuhan reference genome (NC_045512.2). Spatio-temporal analysis indicates that the larger cities of Nabeul, Tunis and Kairouan constituted epicenters for the AY.122 sub-lineage and subsequent dispersion to the rest of the country.DiscussionThis study adds more knowledge about the Delta variant and sub-variants distribution worldwide by documenting genomic and epidemiological data from Tunisia, a North African region. Such results may be helpful to the understanding of future COVID-19 waves and variants.</p