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BackgroundViral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromic surveillance tools, including the WHO Severe Acute Respiratory Infection definition, demonstrate limited diagnostic accuracy in children whose symptom profiles vary widely. This study aimed to develop a machine learning (ML) model to predict microbiologically confirmed vRTIs in hospitalised children and to evaluate performance across age groups and viral pathogens.MethodsWe conducted a retrospective cross-sectional study of 2050 paediatric patients (<18 years) admitted with acute respiratory infections to two tertiary paediatric hospitals in Canada. Predictors included age, sex, hospital transfer status, chronic comorbidity status and 22 presenting symptoms. The primary outcome was microbiologically confirmed vRTI, determined by multiplex PCR or rapid antigen testing. Six ML algorithms were trained and the best-performing model, identified by area under the receiver operating characteristic curve (auROC), was tested on age subgroups, viral pathogens and sites.ResultsAmong 2050 patients (median (IQR) age 2.4 (0.8–5.2) years), 1831 (89.3%) tested positive, most commonly for respiratory syncytial virus (RSV) (38.7%) and enterovirus/rhinovirus (32.8%). Logistic regression with L2 regularisation demonstrated the best performance (auROC, 0.754; 95% CI 0.697 to 0.808; sensitivity, 69.2%; specificity, 69.9%), with greatest performance among children <1 year (auROC, 0.763) and RSV cases (auROC, 0.727).ConclusionsAn ML-based logistic regression model using admission data accurately predicted paediatric vRTIs, outperforming traditional syndromic surveillance definitions, especially among infants <1 year. By integrating ML models into hospital electronic medical records, healthcare systems can achieve enhanced respiratory virus surveillance, faster outbreak detection, greater diagnostic efficiency and improved pandemic preparedness.

The ability to differentiate sporadic primary hyperparathyroidism (sPHPT) caused by a single parathyroid adenoma (PTA) from multiglandular parathyroid disease (MGD) preoperatively, as well as definitely diagnose sPHPT in difficult patients, would enhance surgical decision-making. This work aimed to identify miRNA (miR) signatures for MGD, single- and double-PTA, as well as cell-free miRNA (cfmiR) in plasma samples from patients with single-PTAs to use as biomarkers. A total of 47 patients with sPHPT (single-PTA n = 32, double-PTA n = 12, MGD n = 9). Preoperative plasma samples from 16 single-PTA and 29 normal healthy donors (NHDs). All specimens were processed and analyzed for 2083 miRs using HTG EdgeSeq miR whole-transcriptome assay and normalized using DESeq2 to identify differentially expressed (DE) miRs. MiR classifiers were identified using Random Forest. Main outcome measures were receiver operating characteristic curves and areas under the curve. MiR signatures distinguished normal parathyroid from MGD and PTA as well as MGD from PTA in tissue samples. Common miRs were found in the single-PTA and double-PTAs. Data integration identified a 27-miR signature in single-PTA tissue samples compared to the rest of the tissue samples. In plasma samples analysis, significant cfmiRs were DE in single-PTA patients compared to NHD. Of those, only 9 miRNAs/cfmiRs were found DE both in tissue and plasma samples from patients diagnosed with a single PTA (AUC = 76%). Twenty-seven miRs were consistently found DE in single-PTA tissue and plasma samples. Data integration showed a 9-cfmiR signature with potential clinical utility to preoperatively diagnose sPHPT caused by a single PTA, which could decrease more invasive parathyroid explorations.

BackgroundMolecular alterations impact tumor prognosis and response to treatment. This study was designed to identify transcriptomic and epigenomic signatures of breast cancer (BC) tumors from patients with any prior malignancy.MethodsRNA-sequencing and genome-wide DNA methylation profiles from BCs were generated in the Cancer Genome Atlas project. Patients with secondary breast cancer (SBC) were separated by histological subtype and matched to primary breast cancer controls to create two independent cohorts of invasive ductal (IDC, n = 36) and invasive lobular (ILC, n = 40) carcinoma. Differentially expressed genes, as well as differentially methylated genomic regions, were integrated to identify epigenetically regulated abnormal gene pathways in SBCs.ResultsDifferentially expressed genes were identified in IDC SBCs (n = 727) and in ILC SBCs (n = 261; Wilcoxon’s test; P < 0.05). In IDC SBCs, 105 genes were upregulated and hypomethylated, including an estrogen receptor gene, and 73 genes were downregulated and hypermethylated, including genes involved in antigen presentation and interferon response pathways (HLA-E, IRF8, and RELA). In ILC SBCs, however, only 17 genes were synchronously hypomethylated and upregulated, whereas 46 genes hypermethylated and downregulated. Interestingly, the SBC gene expression signatures closely corresponded with each histological subtype with only 1.51% of genes overlapping between the two histological subtypes.ConclusionsDifferential gene expression and DNA methylation signatures are seen in both IDC and ILC SBCs, including genes that are relevant to tumor growth and proliferation. Differences in gene expression signatures corresponding with each histological subtype emphasize the importance of disease subtype-specific evaluations of molecular alterations.