Fourteen years of the Pregnancy Registry on maternal immunisation with a reduced-antigen-content tetanus-diphtheria-acellular pertussis (Tdap) vaccine

BackgroundGSK initiated a Pregnancy Registry in the United States (US) for the reduced-antigen-content tetanus-diphtheria-acellular pertussis (Tdap; Boostrix, GSK) vaccine with the aim to detect and describe pregnancy outcomes in women vaccinated with Boostrix 28 days before estimated conception or during pregnancy.MethodsVoluntary reports of pregnancy exposure to Boostrix received from spontaneous and post-marketing surveillance sources in the US were assessed. Reports were classified as prospective or retrospective based on the knowledge of pregnancy outcomes at the time of reporting. For completeness, reports of exposure to Boostrix or to the Tdap-inactivated poliovirus vaccine (Boostrix-IPV, GSK) reported to the global safety database from countries outside the US were also evaluated.ResultsFrom May 2005 to August 2019, 1517 (1455 prospective and 62 retrospective) pregnancy reports were received in the Boostrix US Pregnancy Registry. Of the prospective reports, 250 had known outcomes: 244 live infants with no apparent birth defects (BDs), three live infants with BDs, and three spontaneous abortions with no apparent BDs. Of the retrospective reports, 55 had known outcomes: 33 live infants with no apparent BDs, 16 live infants with BDs, one spontaneous abortion with no apparent BDs, four stillbirths with no apparent BDs, and one stillbirth with BDs. Cumulatively, 1321 pregnancy reports (1006 for Boostrix; 315 for Boostrix-IPV) were received from countries outside the US. Of these, 163 prospective reports and 551 retrospective reports had known outcomes. Results were in line with those from the Boostrix US Pregnancy Registry.ConclusionsData currently available from the Boostrix US Pregnancy Registry and from countries outside the US suggested that exposure to Boostrix or Boostrix-IPV during pregnancy does not raise safety concerns related to adverse pregnancy outcomes or BDs.     

A familiar species of Crank: Anti-Vaccinationists in medical history

Vaccination, health psychology and mental health make for three well-established and prestigious topoi in medical history. An in-depth look at their historical intersections remains forthcoming, however. Vaccinology’s psychological turns merit historians’ attention, all the more in the light of more recent, post-psychological and infodemiological, perspectives in vaccine acceptance research. Historiography at this point may help appreciating the present, and future, standing of psychological profiling in terms of its explanatory merits and policy uses. Of specific, critical interest is the motif of mental illness historically shared by vaccine advocates and contrarians. Mock-psychiatric nosology was a favored framing device for vaccination polemicists early on, indeed before vaccines were called vaccines and before psychiatry came to be called psychiatry. Though long anticipated, substantive historical-sociological and empirical approaches to vaccine non-acceptance were seen only from the 1920s and 1930s, respectively. Today, spirited animosity over vaccination continues to invite both professional and public debate about the founding concepts, the basic tenets, and the defining boundaries, of the mental health sciences.     

液质联用技术联合网络药理学探讨白鲜皮治疗湿疹的作用机制＊

目的 采用液质联用技术分析白鲜皮中的主要化学成分；基于网络药理学技术研究白鲜皮治疗皮肤湿疹的活性成分、作用靶点及作用机制。方法 利用液质联用技术（LC-MS）对白鲜皮主要成分进行分析，通过质谱数据结合文献资料，对各主要成分进行快速识别；通过利用SwissADME数据库对白鲜皮潜在活性成分进行预测，筛选主要作用靶点。进行KEGG通路富集分析，最终得到白鲜皮成分-靶点-疾病-通路网络图。采用分子对接技术对主要成分和作用靶点进行验证。结果 从白鲜皮中共分析鉴定了12种成分，进一步筛选得到4种潜在活性成分，可作用于48个关键靶点和14条主要通路。结论 采用LC-MS技术结合网络药理学方法，可以快速分析白鲜皮的潜在活性成分，并初步揭示了成分-靶点-通路之间的关系。通过分子对接技术可以做作用靶点进行验证。     

An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy

A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T₁, T₂, and contrast enhanced T₁ (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128 × 192 × 192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3 Houndsfield unit (HU) when trained using T1 images, 71.1/186.1 HU for T2, and 82.9/236.4 HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2 cm and for 98% of all spots the difference was less than 1 cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. “real-world data”).     

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net–based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.     

Retrospective study on the predictive factors in chronic trismus

Trismus is one of the most debilitating and treatment-resistant complications resulting from head and neck oncological treatments. The objective of this study was to assess how primary tumour variables could assist in predicting chronic trismus. From a (retrospective) oncological database (Department of Oral and Maxillofacial Surgery, University Hospitals Leuven), tumour-related, surgical, and oral functional variables were reviewed. Contributing factors for chronic trismus, defined as a mouth opening of less than 35 mm, at least one year after treatment for oral squamous cell cancer, were assessed via logistic regression. A mediational analysis was conducted on the significant predictive variables. Thirteen out of 52 patients were observed to have chronic trismus. A significantly higher prevalence of trismus was found for increasing clinical T classification (p < 0.01), tumours based in the maxilla or the retromolar trigone (p = 0.04), after adjuvant radiotherapy (p = 0.04), and/or with masticatory muscle tumour invasion (p ≤ 0.02). Furthermore, radiotherapy significantly impacted T classification in chronic trismus, while T classification was significantly related to masticatory muscle invasion. Although radiotherapy and clinical T classification are well-established risk factors for postoperative trismus, masticatory muscle invasion should be considered as one of the main predictive factors.     

The social vulnerability metric (SVM) as a new tool for public health

Objective

To derive and validate a new ecological measure of the social determinants of health (SDoH), calculable at the zip code or county level.

Data Sources and Study Setting

The most recent releases of secondary, publicly available data were collected from national U.S. health agencies as well as state and city public health departments.

Study Design

The Social Vulnerability Metric (SVM) was constructed from U.S. zip-code level measures (2018) from survey data using multidimensional Item Response Theory and validated using outcomes including all-cause mortality (2016), COVID-19 vaccination (2021), and emergency department visits for asthma (2018). The SVM was also compared with the existing Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to determine convergent validity and differential predictive validity.

Data Collection/Extraction Methods

The data were collected directly from published files available to the public online from national U.S. health agencies as well as state and city public health departments.

Principal Findings

The correlation between SVM scores and national age-adjusted county all-cause mortality was r = 0.68. This correlation demonstrated the SVM's robust validity and outperformed the SVI with an almost four-fold increase in explained variance (46% vs. 12%). The SVM was also highly correlated (r ≥ 0.60) to zip-code level health outcomes for the state of California and city of Chicago.

Conclusions

The SVM offers a measurement tool improving upon the performance of existing SDoH composite measures and has broad applicability to public health that may help in directing future policies and interventions. The SVM provides a single measure of SDoH that better quantifies associations with health outcomes.