Preferences and willingness of accepting COVID-19 vaccine booster: Results from a middle-income country

The recent wave of COVID-19 cases has led to the potential need for booster doses. We surveyed 6,294 people and found that 87.6% reported willingness to take a booster dose, with vaccine efficacy rate being the most common reason cited to accept booster dose. Differences in acceptance rates were noted among those working in non-health related sectors, different ethnic groups as well as those who had taken viral vector vaccines.     

女性致密性骨炎血清骨转换生化标志物水平变化研究

背景 致密性骨炎（OCI）和其他疾病有时难以鉴别，探讨血清骨转换生化标志物可为OCI的鉴别诊断提供依据。 目的 探索女性OCI患者的血清骨转换生化标志物的水平变化及临床意义。 方法 回顾性选取2013年6月至2022年2月在北京积水潭医院门诊及住院诊断为OCI的61例女性患者作为观察组，年龄15~50岁，平均（33.8±6.6）岁，病程2周~15年。选择同期61例女性体检健康者作为对照组，年龄15~48岁，平均（35.6±7.6）岁。比较两组一般临床资料和血清骨转换生化标志物水平，并对血清骨转换生化标志物与病情相关指标进行相关性分析。 结果 观察组血清白蛋白（45.4±2.9）g/L低于对照组（46.5±2.8）g/L（t=2.190，P<0.05）。血清骨转换生化标志物比较结果显示，观察组血清1型胶原羧基末端肽β特殊序列（β-CTX）〔0.28（0.23，0.37）μg/L〕、N-端骨钙素（OC）〔13.1（11.2，16.2）μg/L〕、25-羟维生素D3〔25-（OH）VD3〕〔（14.1±5.1）μg/L〕低于对照组〔0.36（0.29，0.48）μg/L，15.6（13.7，17.3）μg/L，（17.5±6.6）μg/L〕（Z=-2.983、-3.255，t=3.081，P<0.05）。长病程亚组OC水平〔14.6（12.4，18.5）μg/L〕高于短病程亚组〔11.7（10.2，14.0）μg/L〕（Z=-2.407，P<0.05）。多孕亚组β-CTX〔0.25（0.22，0.32）μg/L〕、OC水平〔12.2（10.3，15.0）μg/L〕低于非多孕亚组〔0.33（0.26，0.44）μg/L、13.4（12.0，18.8）μg/L〕（Z=-2.486、-1.897，P<0.05）。相关性分析显示，观察组血清1型前胶原氨基端延长肽（tP1NP）与妊娠次数、生产次数均呈负相关（rs=-0.276、-0.298，P<0.05），OC与体质指数（BMI）、视觉模拟评分法（VAS）评分、妊娠次数均呈负相关（rs=-0.284、-0.374、-0.360，P<0.05），25-（OH）VD3水平与BMI呈正相关（rs=0.275，P<0.05）。 结论 女性OCI患者血清OC、β-CTX水平明显降低，可为鉴别其他疾病提供依据；血清OC水平可以反映OCI患者的严重程度，同时OC水平与患者妊娠次数相关；tP1NP与妊娠次数、生产次数相关。     

NEMA NU 2-2018标准在PET/CT系统性能测试中的作用评价

目的 评价美国国家电器制造协会(National Electrical Manufactures Association, NEMA)最新标准(NU 2-2018)在正电子发射型计算机断层显像/电子计算机断层显像(positron emission tomography/computed tomography, PET/CT)设备性能检测中的作用。 方法 依据最新的NEMA NU 2-2018标准,检测西门子Biograph Vision PET/CT的空间分辨率、灵敏度、散射分数、计数丢失、随机符合、飞行时间分辨率、计数丢失率和随机符合校正精度、图像质量、衰减和散射校正精度及PET与CT配准精度指标。 结果 距视野中心1 cm处横向和轴向空间分辨率分别为3.75 mm和3.76 mm;在视野中心和轴向10 cm处的灵敏度分别为16.83 kcps/MBq和16.67 kcps/MBq;放射性浓度为27.37 kBq/mL时,最大等效噪声计数率为258.26 kcps,散射分数为38.58%;系统时间分辨率为209.82 ps;图像质量模型的对比度恢复系数范围为88.9%~96.2%,背景变异系数范围为2.05%~6.80%,平均肺插件残余误差为2.43%;计数丢失和随机符合校正最大误差为3.9%;距离床板末端 5 cm 和 100 cm处,在距视野中心Y轴1 cm处,PET和CT的配准精度分别为0.46 mm和1.07 mm,在距视野中心X轴20 cm处,PET和CT的配准精度分别为1.06 mm和1.45 mm,在距视野中心Y轴20 cm处PET和CT的配准精度分别为0.85 mm和1.15 mm。 结论 NEMA NU 2-2018标准检测条件更加接近临床,能更好地反映PET/CT设备的系统性能。     

Motivational interviewing for maternal Immunizations: Intervention development

Background and ObjectiveVaccine uptake during pregnancy remains low. Our objectives were to describe 1) development and adaptation of a clinician communication training intervention for maternal immunizations and 2) obstetrics and gynecology (ob-gyn) clinician and staff perspectives on the intervention and fit for the prenatal care context.MethodsDesign of the Motivational Interviewing for Maternal Immunizations (MI4MI) intervention was based on similar communication training interventions for pediatric settings and included presumptive initiation of vaccine recommendations (“You’re due for two vaccines today”) combined with motivational interviewing (MI) for hesitant patients. Interviews and focus group discussions were conducted with ob-gyn clinicians and staff in five Colorado clinics including settings with obstetric physicians, certified nurse midwives (CNMs), and clinician-trainees. Participants were asked about adapting training to the ob-gyn setting and their implementation experiences. Feedback was incorporated through iterative changes to training components.ResultsInterview and focus group discussion results from participants before (n = 3), during (n = 11) and after (n = 25) implementation guided intervention development and adaptation. Three virtual, asynchronous training components were created: a video and two interactive modules. This virtual format was favored due to challenges attending group meetings; however, participants noted opportunities to practice skills through role-play were lacking. Training modules were adapted to include common challenging vaccine conversations and live-action videos. Participants liked interactive training components and use of adult learning strategies. Some participants initially resisted the presumptive approach but later found it useful after applying it in their practices. Overall, participants reported that MI4MI training fit well with the prenatal context and recommended more inclusion of non-clinician staff.ConclusionsMI4MI training was viewed as relevant and useful for ob-gyn clinicians and staff. Suggestions included making training more interactive, and including more complex scenarios and non-clinician staff.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.