Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

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Effectiveness of virtual reality in the treatment of hand function in children with cerebral palsy: A systematic review

Study DesignSystematic review.IntroductionChildren with cerebral palsy (CP) may have limited use of their hands for functional activities and for fine motor skills. Virtual reality (VR) is a relatively new and innovative approach to facilitate hand function in children with CP.Purpose of the StudyThe primary purpose of this study was to determine the effectiveness of VR as an intervention to improve hand function in children with CP compared to either conventional physiotherapy or other therapeutic interventions. The secondary purpose was to classify the outcomes evaluated according to the International Classification of Functioning, Disability and Health (ICF) dimensions.MethodsA International prospective register of systematic reviews (PROSPERO)-registered literature search was carried out in August 2015 in MEDLINE, CINAHL, ERIC, HealthSTAR, AMED, BNI, Embase, PsycINFO, PEDro, Cochrane Central Register, DARE, OTSeeker, REHABDATA, HaPI, CIRRIE, and Scopus. PRISMA guidelines were followed. Only randomized controlled trials (RCTs) were included, and their methodological qualities were examined using the Cochrane collaboration's risk of bias (RoB) tool. A narrative synthesis was performed.ResultsThe 6 RCTs published on this topic provide conflicting results. Four studies reported improved hand function (2 low RoB, 1 high RoB, and 1 unclear RoB), whereas 2 studies reported no improvement. All of the RCTs reported the activity element of ICF, but no study explicitly described the effect of VR intervention based on the ICF model.ConclusionThe role of VR ti imrpove hand fucntion in children with CP is unclear due to limited evidence; use as an adjunct has some support.     

Annular versus supra-annular sizing for transcatheter aortic valve replacement in bicuspid aortic valve disease

BackgroundCT measurement of supra-annular area (SA) has been proposed as an alternative to annular area (AA) for sizing of trancatheter valves in biscuspid aortic valves (BAV). This study examines the reproducibility of SA and AA measurements and their potential impact on downstream transcatheter heart valve sizing and clinical outcomes.Methods44 consecutive patients (mean age: 73 ± 15 years, 57% male) undergoing CTA with subsequent SAPIEN 3 valve insertion for severe bicuspid aortic stenosis (AS) were included. AA was measured at the basal ring. SA was measured by generating a circle defined by the intercommisural distance. AA and SA were measured by 2 independent observers. Baseline characteristics, TAVR procedural data, and discharge echocardiography data were collected.ResultsThe SA was significantly larger than the AA (562 ± 146mm2 vs. 518 ± 112mm2,p = 0.013). Interobserver agreement was high using both techniques (ICC AA = 0.98,p < 0.001; SA = 0.80,p < 0.001), but with narrower limits of agreement with AA measurements (mean difference (limits of agreement): AA = −3mm2 (22; 19), SA = −16mm2 (−92; 76)). AA-based device sizing demonstrated substantial agreement with final valve inserted (κ = 0.72,p < 0.001), while SA demonstrated fair agreement (κ = 0.40,p < 0.001). There was no difference in post TAVR gradients, paravalvular leakage or valve success between patients with concordant sizing between AA and SA, and those in whom SA would have suggested an alternate valve size.ConclusionsSupra-annular sizing is less reproducible than annular sizing, with no difference in procedural complication rates in patients in whom supra-annular sizing would have altered the device size used. These results suggest no role for supra-annular sizing in current clinical practice.     

体视学法在颅内血肿体积测量的应用

目的介绍改进后的体视学法测量颅内血肿体积,并验证其准确性。方法在92例颅内出血的CT图像上,同时运用体视法、CT定量法测量颅内血肿体积,以CT定量法的结果为标准验证体视法的准确性。结果按血肿体积大小分为5组,各组CT定量法、体视学法所测得体积采用配对样本的t检验分析P值皆大于0.05,即CT定量法、体视学法所测血肿体积无统计学差异。结论体视学法测量颅内血肿准确可靠,在CT工作站对颅内血肿病例行胶片打印排版时,在图像上加上合适的网格,可方便临床医生快速运用体视法准确测量颅内血肿体积。     

Impact of the COVID-19 lockdown on digital device-related ocular health

Purpose:Since the declaration of the lockdown due to COVID-19, the usage of digital devices has gone up across the globe, resulting in a challenge for the visual systems of all ages. The purpose of this study is to assess the impact of the lockdown on digital device usage, and consequently, the ocular surface health implications and circadian rhythm abnormalities related to digital eye strain.Methods:An open online survey was sent through various social media platforms and was open for a period of 2 weeks.Results:A total of 407 usable responses were obtained; the average age of respondents was 27.4 years. Typically, 93.6% of respondents reported an increase in their screen time since the lockdown was declared. The average increase in digital device usage was calculated at about 4.8 ± 2.8 h per day. The total usage per day was found to be 8.65 ± 3.74 hours. Sleep disturbances have been reported by 62.4% of people. Typically, 95.8% of respondents had experienced at least one symptom related to digital device usage, and 56.5% said that the frequency and intensity of these symptoms increased since the lockdown was declared.Conclusion:The study highlighted the drastic increase in use of digital devices after the initiation of the COVID-19 lockdown, and along with it, the slow deterioration of ocular health across all age groups. Awareness about prevention of digital eye strain should be stressed, and going forward, measures to bring these adverse effects to a minimum should be explored.     

Adverse events due to unnecessary radiation exposure in medical imaging reported in Finland

IntroductionAdverse events in radiology are quite rare, but they do occur. Radiation safety regulations and the law obligate organizations to report certain adverse events, harm and near misses, especially events related to patients' health and safety. The aim of this study was to describe and analyse incidents related to radiation safety issues reported in Finland.MethodsThe data were collected from incident reports documented by radiology personnel concerning notifications of abnormal events in medical imaging made to the Radiation and Nuclear Safety Authority between 2010 and 2017. During these eight years, 312 reports were submitted. Only events reported from radiology departments were included; nuclear medicine, radiotherapy and animal radiology cases were excluded. The final number of reports was 293 (94%).ResultsThe majority of the 293 approved reports were related to computed tomography (CT, 68.3%) and to X-ray examinations (27.6%). Altogether 82.9% of those irradiated were adults, most of whom were exposed to unnecessary radiation through CT (86.5%), 5.5% were children, and 4.4% pregnant women. The most common effective dose of unnecessary radiation was 1 mSv or less (89.7% of all examinations). The highest effective doses were reported in CT (from under 1 mSv–20 mSv and above). The reasons for the adverse events were incorrect identification (32%), incorrect procedure, site or side (30%); and human errors or errors of knowledge (20%).ConclusionAdverse events occurred especially in CT examinations. It is important to collect and analyse incident data, assess the harmful events, learn from them and aim to reduce adverse events.Implications for practiceThis study emphasizes the need for radiological personnel to obtain evidence-based information on adverse events and focus on training to improve patient safety.     

Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier

Objective: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer at the early stage. It requires radiologist and special doctors to find the tumoral tissue of the lung cancer. For this reason, the recommended work helps to segment the tumoral tissue of CT lung image in an effective way. Methods:  The research work uses hybrid segmentation technique to separate the lung cancer cells to diagnose the lung tumour. It is a technique which combines active contour along with Fuzzy c means to diagnose the tumoral tissue. Further the segmented portion was trained by Convolutional Neural Network (CNN) in order to classify the segmented region as normal or abnormal. Results: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. In future the other techniques can be utilized to improve the details before segmentation. The proposed work provides 96.67 % accuracy. Conclusion: Hybrid segmentation technique involves several steps like preprocessing, binarization, thresholding, segmentation and feature extraction using GLCM.     

The effect of different statistical approaches on image quality data obtained from radiological examinations

IntroductionSelection of optimal image acquisition protocols in medical imaging remains a grey area, the superimposed use of the Likert scale in radiological image quality evaluations creates an additional challenge for the statistical analysis of image quality data.Using a simulation study, we have trialled a novel approach to analysing radiological image quality Likert scale data.MethodsA simulation study was undertaken where simulated datasets were generated based on the distribution of Likert scale values according to varying image acquisition protocols from a real dataset. Simulated Likert scale values were pooled in four different ways; the mean, median, mode and the summation of patient Likert scale values of which the total was assigned a categorical Likert scale value. Estimates of bias, MAPE and RMSPE were then calculated for all four pooling approaches to determine which method most accurately represented an expert's opinion.ResultsWhen compared to an expert's opinion, the method of summation and categorisation of Likert scale values was most accurate 49 times out of the 114 (43.0%) tests. The mean 28 times out of 114 (24.6%), the median 23 times out of 114 (20.2%) and the mode 17 times out of 114 (14.9%).ConclusionWe conclude that our method of summation and categorisation of Likert scale values is most often the best representation of the simulated data compared to the expert's opinion.Implications for practiceThere is scope to reproduce this simulation study with multiple observers to reflect clinical reality more accurately with the dynamic nature of multiple observers. This also prompts future investigation into other anatomical areas, to see if the same methods produce similar results.