Clinical and ultrasonographic features in cancer risk stratification of indeterminate thyroid nodules

Objectives:To estimate the risk of malignancy in indeterminate thyroid nodules and to determine whether certain clinical or radiological parameters can predict the risk of malignancy.Methods:This retrospective study enrolled all adult patients (age ≥14 years) with a cytological diagnosis of atypia/follicular lesion of undetermined significance and follicular neoplasm/suspicious for a follicular neoplasm between January 2014 and January 2020. Fifty patients with surgically treated primary thyroid nodules, documented final histological diagnosis, and ultrasound examination records were included. Thyroid nodules were evaluated radiologically using Thyroid Imaging Reporting and Data System introduced by the American College of Radiology (2017).Results:Forty-two (84.0%) female and 8 (16.0%) male patients were enrolled in the study. The malignancy risks were 44.8% for Bethesda III and 28.6% for Bethesda IV. The malignancy risks for the Thyroid Imaging Reporting and Data System categories were 33.3% (TR2), 39.1% (TR3), 35.3% (TR4), and 50% (TR5). No significant associations were observed between age, gender, Bethesda category, and Thyroid Imaging Reporting and Data System and the risk of malignancy.Conclusion:None of the clinical or radiological characteristics evaluated in this study contributed to the cancer risk stratification of thyroid nodules with indeterminate cytology. A prospective multicenter study is needed to better understand cytologically indeterminate thyroid nodules.     

Psychometric Analysis for fear of COVID-19 Scale (FCV-19S) and its association with depression in patients with diabetes: A cross sectional study from a Tertiary Care Centre in Karachi,Pakistan

Background and aimsTo assess the psychometric properties of the Fear of COVID-19 (FCV–19S) scale and to determine its associated factors among the Pakistani patients with diabetes.MethodsThis observational study was conducted in 24-h helpline service, a department of Baqai Institute of Diabetology and Endocrinology (BIDE). Study duration was from August to September 2020. The target population was registered adult patients with type 2 diabetes aged >16 years. Baseline demographic details were obtained from hospital management system of BIDE. Forward-backward translation method was used to translate the existing Fear scale (FCV–19S). Symptoms of depressive disorder were assessed through Patient Health Questionnaire (PHQ9).ResultsTotal of 380 participants with mean age 51.93 ± 12.03 years contributed in the study. Three factors loading and item correlation of fear COVID-19 explained 96% of total variance having unidimensional Cronbach’s alpha of 0.881. All demographic indicators that showed significance in univariate model were included in multivariate model. Females had more fear for COVID-19 compared to males (OR = 1.73, 95% CI (1.15–2.6)), whereas current smokers had also showed 4 times more fear than non-smokers (OR = 4.19, 95% CI (1.18–14.83). Depression assessed by PHQ9 showed maximum fear of COVID-19 in participants with moderate depression.ConclusionFCV-19S had adequate psychometric properties for assessing effects of pandemic in people with diabetes attending tertiary care center.     

Maturation of nasal microbiota and antibiotic exposures during early childhood: a population-based cohort study

ObjectivesLittle is known about maturation of the airway microbiota during early childhood and the consequences of early-life antibiotic exposure.MethodsIn a population-based birth cohort of 902 healthy Finnish children, we applied deep neural network models to investigate the relationship between the nasal microbiota (measured by 16S rRNA gene sequencing at up to three time points) and child age during the first 24 months. We also performed stratified analyses according to antibiotic exposure during the age period 0–2 months.ResultsThe dense deep neural network analysis successfully modelled the relationship between 232 bacterial genera and child age with a mean absolute error of 4.3 (95%CI 4.0–4.7) months. Similarly, the recurrent neural network analysis also successfully modelled the relationship between 215 genera and child age with a mean absolute error of 0.45 (95%CI 0.42–0.47) months. Among the genera, Staphylococcus spp. and members of the Corynebacteriaceae decreased with age, while Dolosigranulum and Moraxella increased with age in the first 2 years of life (all false discovery rate (FDR) = 0.001). In children without early-life antibiotic exposure, Dolosigranulum increased with age (FDR = 0.001). By contrast, in those with early-life antibiotic exposure, Haemophilus increased with age (FDR = 0.002).ConclusionsIn this prospective birth cohort of healthy children, we demonstrated the development of the nasal microbiota, with shifts in specific genera constituting maturation, in the first 2 years of life. Antibiotic exposures during early infancy were related to different age-discriminatory bacteria.     

Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model

Journal of Digital Imaging - Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture...