The aim of this study was to investigate the pneumatization degree of ethmomaxillary sinus (EMS) and adjacent structures, and its impact on chronic rhinosinusitis (CRS).
 
  A retrospective analysis of paranasal sinus CT scans of 996 patients was conducted. The maximum vertical diameter of EMS in the coronal plane was measured, allowing EMS to be classified, and its impact on ipsilateral CRS were examined.
 
  The prevalence of EMS was 11.9%. The maximum vertical diameter of EMS in the coronal plane ranged from 3.68 to 28.76mm with a mean (± SD) of 11.32 ± 5.12mm. The prevalence rates of EMS in CRS sides and non-CRS sides were 12.5% and 9.3%, respectively, which was significantly different (χ
   = 4.495; p < 0.05). The difference in prevalence between the three types of EMS in ipsilateral CRS was statistically significant (χ
   = 6.733; p < 0.05). The difference in Lund-Mackay (LM) score of ipsilateral CRS between the three types showed no statistically significant difference (H = 4.033; p > 0.05).
 
  EMS is a common anatomical variation with marked individual differences in shape and pneumatization degree. A higher degree of EMS pneumatization may contribute to the occurrence of CRS; this should be investigated before surgery.
 EMS is a common anatomical variation with marked individual differences in shape and pneumatization degree. A higher degree of EMS pneumatization may contribute to the occurrence of CRS; this should be investigated before surgery.
  The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.
 
  In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).
 
  The radiation dose in terms of CT dose index (CTDI
  ) was reduced by up to 89%. The RM Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.
 • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.An emerging theoretical framework suggests that neural functions associated with stereotyping and prejudice are associated with frontal lobe networks. Using a novel neuroimaging technique, functional near-infrared spectroscopy (fNIRS), during a face-to-face live communication paradigm, we explore an extension of this model to include live dynamic interactions. Neural activations were compared for dyads of similar and dissimilar socioeconomic backgrounds. The socioeconomic status of each participant was based on education and income levels.  https://www.selleckchem.com/products/lenalidomide-s1029.html  Both groups of dyads engaged in pro-social dialectic discourse during acquisition of hemodynamic signals. Post-scan questionnaires confirmed increased anxiety and effort for high-disparity dyads. Consistent with the frontal lobe hypothesis, left dorsolateral pre-frontal cortex (DLPFC), frontopolar area and pars triangularis were more active during speech dialogue in high than in low-disparity groups. Further, frontal lobe signals were more synchronous across brains for high- than low-disparity dyads. Convergence of these behavioral, neuroimaging and neural coupling findings associate left frontal lobe processes with natural pro-social dialogue under 'out-group' conditions and advance both theoretical and technical approaches for further investigation.Dengue is an important global health problem and is endemic in many developing and developed countries. Transmission of dengue may occur in several ways and information on transfusion-transmitted dengue is limited. We conducted a literature search on transfusion-related dengue using the PubMed, Scopus, Embase and Google Scholar databases and have summarized the findings. A number of apparently healthy blood donors have been found to be infected with the dengue virus (DENV) and thus may transmit the virus onto recipients of this blood. It is not possible to identify and exclude such donors at the donor selection stage and thus reliable screening tests should be available in highly endemic areas to ensure a safe blood supply.
  The Defence Forces' members are exposed to high-level noise that increases their risk of hearing loss (HL). Besides military noise, the other risk factors include age and gender, ototoxic chemicals, vibration, and chronic stress. The current study was designed to study the effects of personal, work conditions-related risk factors, and other health-related traits on the presence of hearing problems.
 
  A cross-sectional study among active military service members was carried out. Altogether, 807 respondents completed a questionnaire about their health and personal and work-related risk factors in indoor and outdoor environments. The statistical analysis was performed using statistical package of social sciences (descriptive statistics) and R (correlation and regression analysis) software.
 
  Almost half of the active service members reported HL during their service period. The most important risk factors predicting HL in the military appeared to be age, gender, and service duration. Also, working in a noisy environment with exposure to technological, vehicle, and impulse noise shows a statistically significant effect on hearing health.