Acute gastroenteritis (AGE) remains, throughout Europe, a public health issue. Under the age of 5, some 20 to 30% of bacterial microorganisms are identified. However, cost-effectiveness of routine stool cultures yielding only 2% results preclude routine stool culturing.
Objective
Evaluation of the value of stool culture of children with AGE.
Methods
Retrospective data collection from clinical records of patients less than 18 years old submitted to stool cultures over a one year period.
Results
Out of 322 stool culture, 56.8% fulfilled the accepted ESPGHAN criteria and 74.8% had at least 1 clinical predictor of positivity (fever, blood or mucus stools, > 10 bowel actions/24 h, abdominal pain, travelling to highly epidemic countries). There were 121 positive cultures positive, 79.3% in patients obeying the defined criteria and 91.7% with clinical predictors of positivity. Campylobacter was the most frequently identified agent (68.6%), followed by Salmonella. Campylobacter decreased within an increasing age whilst Salmonella showed an inverse pattern. Campylobacter was the most frequently identified agent throughout all seasons of the year, followed by Salmonella, except in the winter when Yersinia took the second place.
Discussion
Sticking to accepted criteria for stool collection and/or to defined clinical features, increasing the yield of stool cultures.
In September 2020, we tested 13,398 persons in Portugal for antibodies against severe acute respiratory syndrome coronavirus 2 by using a quota sample stratified by age and population density. We found a seroprevalence of 2.2%, 3-4 times larger than the official number of cases at the end of the first wave of the pandemic.
Neonatal diabetes mellitus (diabetes mellitus occurring before 6 months of age) is a rare disease caused in most cases by mutations at specific genetic loci. A 3-month old toddler, developed irritability and labored breathing. On initial presentation to her local emergency department, she was afebrile, heart rate was 120 bpm, BP: 98/56 mmHg, and respiratory rate: 64 bpm. Physical examination was notable for prostration, dehydration and rapid deep breathing. Initial laboratory work revealed a glucose level of 1040 mg/dL, a venous pH of 7.08, and the presence of glucose and ketones in her urine. She received normal saline bolus and she was transported to our hospital. Upon her arrival, she spent four days in the Intensive Care Pediatric Unit until estabilization. At the fifth day she was transferred to the hospital ward were she began subcutaneous insulin. Her abdominal ultrasound revealed a normal pancreas. C Peptide was 0.47 ng/ml and HbA1c was 13.9%. The patient was discharged after 26 days on 2.5+1.5 units of intermediate insulin daily, with an additional 0.5–2 units daily of insulin aspartic as required to treat hyperglycemia. Testing for mutations associated with permanent neonatal diabetes identified a mutation in the KCNJ11 gene. There have been reports of the successful transition from insulin to sulfonylurea agents in patients with PND caused by mutations in the KCNJ11 gene. The Hospital Sao Joao Ethics Commission has been asked about the beginning of sulfonylurea and we hope to successfully change the diabetic medication into an oral one.
Abstract Telomere fusions (TFs) can trigger the accumulation of oncogenic alterations leading to malignant transformation and drug resistance. Despite their relevance in tumour evolution, our understanding of the patterns and consequences of TFs in human cancers remains limited. Here, we characterize the rates and spectrum of somatic TFs across >30 cancer types using whole-genome sequencing data. TFs are pervasive in human tumours with rates varying markedly across and within cancer types. In addition to end-to-end fusions, we find patterns of TFs that we mechanistically link to the activity of the alternative lengthening of telomeres (ALT) pathway. We show that TFs can be detected in the blood of cancer patients, which enables cancer detection with high specificity and sensitivity even for early-stage tumours and cancers of high unmet clinical need. Overall, we report a genomic footprint that enables characterization of the telomere maintenance mechanism of tumours and liquid biopsy analysis.
Abstract Accurate detection of somatic structural variants (SVs) and copy number aberrations (SCNAs) is critical to inform the diagnosis and treatment of human cancers. Here, we describe SAVANA, a computationally efficient algorithm designed for the joint analysis of somatic SVs, SCNAs, tumour purity and ploidy using long-read sequencing data. SAVANA relies on machine learning to distinguish true somatic SVs from artefacts and provide prediction errors for individual SVs. Using high-depth Illumina and nanopore whole-genome sequencing data for 99 human tumours and matched normal samples, we establish best practices for benchmarking SV detection algorithms across the entire genome in an unbiased and data-driven manner using simulated and sequencing replicates of tumour and matched normal samples. SAVANA shows significantly higher sensitivity, and 9- and 59-times higher specificity than the second and third-best performing algorithms, yielding orders of magnitude fewer false positives in comparison to existing long-read sequencing tools across various clonality levels, genomic regions, SV types and SV sizes. In addition, SAVANA harnesses long-range phasing information to detect somatic SVs and SCNAs at single-haplotype resolution. SVs reported by SAVANA are highly consistent with those detected using short-read sequencing, including complex events causing oncogene amplification and tumour suppressor gene inactivation. In summary, SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably in clinical samples.
Abstract Importance Drug shortages leave affected patients in a vulnerable position. Objective To describe incidence and prevalence of use for medicines with suggested shortages in at least one European country, as announced by the European Medicines Agency, and to characterise the users of these drugs including the indication of use, duration of use, and dosage. Design We performed a descriptive cohort study from 2010 and up to 2024 in a network of databases which have mapped their data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Setting Settings included primary care, secondary care, claims and various disease registries. Participants We included all patients with at least 365 days of history on the database. Exposures All medicines with a suggested shortage in at least one European country for more than 365 days (n=18). We also assessed their key alternatives (n=39). Main outcomes and measures We estimated annual incidence rates and period prevalence. A drop in incidence or prevalence of >33% after the shortage was announced was considered confirmation of a shortage. Results Among 52 databases from Europe and the United States, we observed shortages according to decreased incidence of use for 8 drugs and shortages according to prevalence of use for 9 drugs. The drugs varenicline and amoxicillin alone or plus clavulanate were in shortage in the most number of countries. Conclusion and relevance We compiled and analysed data of annual incidence and prevalence of use plus information on patient characteristics, indication, and dose for 57 medicines among 52 databases in Europe and the United States between 2010 and 2024. We detected shortages and observed a change in the users’ characteristics for several drugs. We have described timely real-world scenarios of drug shortages and those unobserved in various health care settings and countries which helps to better understand how drug shortages play out in real life.
SARS-CoV-2 has emerged as a human pathogen, causing clinical signs, from fever to pneumonia-COVID-19-but may remain mild or asymptomatic. To understand the continuing spread of the virus, to detect those who are and were infected, and to follow the immune response longitudinally, reliable and robust assays for SARS-CoV-2 detection and immunological monitoring are needed. We quantified IgM, IgG, and IgA antibodies recognizing the SARS-CoV-2 receptor-binding domain (RBD) or the Spike (S) protein over a period of 6 months following COVID-19 onset. We report the detailed setup to monitor the humoral immune response from over 300 COVID-19 hospital patients and healthcare workers, 2500 University staff, and 198 post-COVID-19 volunteers. Anti-SARS-CoV-2 antibody responses follow a classic pattern with a rapid increase within the first three weeks after symptoms. Although titres reduce subsequently, the ability to detect anti-SARS-CoV-2 IgG antibodies remained robust with confirmed neutralization activity for up to 6 months in a large proportion of previously virus-positive screened subjects. Our work provides detailed information for the assays used, facilitating further and longitudinal analysis of protective immunity to SARS-CoV-2. Importantly, it highlights a continued level of circulating neutralising antibodies in most people with confirmed SARS-CoV-2.
Abstract Whole-genome sequencing (WGS) of human cancers has revealed that structural variation, which refers to the rearrangement of the genome leading to the deletion, amplification of reshuffling of DNA segments ranging from a few hundred bp to entire chromosomes, is a key mutational process in cancer evolution. Notably, pan-cancer analyses have revealed that both simple and complex forms of structural variation are pervasive across diverse human cancers, and often underpin drug resistance and metastasis. To date, the study of cancer genomes has relied on the analysis of short-read WGS on the dominant Illumina platform, which generates short, highly-accurate reads of 100-300bp that allow the study of point mutations at high resolution. However, detection of structural variants (SVs) using short reads is limited, as breakpoints falling in repetitive regions cannot be reliably mapped to the human genome. As a result, our understanding of the patterns and mechanisms underpinning structural variation in cancer genomes remains incomplete. In contrast to short-read sequencing, long-read sequencing technologies, such as Oxford Nanopore and PacBio, permit continuous reading of individual DNA molecules over 10 kilobases, thus providing unparalleled information to resolve SVs in repetitive regions and complex genome rearrangements. However, novel bioinformatics methods that account for the higher error rate of long-read methods are needed to take advantage of their capabilities for cancer genome analysis. Here, we present SAVANA, a novel structural variant caller for long-read sequencing data specifically designed for the analysis of cancer genomes. To identify both somatic and germline SVs, SAVANA takes as input long-read WGS data from a tumor and normal sample pair. SAVANA scans sequencing reads to detect split reads and gapped alignments, which are then clustered to define putative SVs. Next, SAVANA applies a machine learning-informed set of heuristics to remove false positives arising from mapping errors and sequencing artifacts. Extensively validated against a multi-platform truthset, we show that SAVANA identifies a range of somatic rearrangements with high recall and precision, outperforming existing tools while maintaining a lower execution time than competing methods. In patient samples, SAVANA identifies clinically relevant alterations, such as oncogenic gene fusions, with high accuracy. Additionally, SAVANA permits the reconstruction of double minutes, multi-chromosomal chromothripsis events, and SVs mapping to highly repetitive regions, including centromeres. In sum, SAVANA permits the characterization of complex structural variants and can uncover clinically relevant mutations across diverse cancer types with high accuracy. Citation Format: Hillary Elrick, Jose Espejo Valle-Inclan, Katherine E. Trevers, Francesc Muyas, Rita Cascão, Angela Afonso, Cláudia C. Faria, Adrienne M. Flanagan, Isidro Cortés-Ciriano. SAVANA: a computational method to characterize structural variation in human cancer genomes using nanopore sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB080.
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