Optimal Respiratory Syncytial Virus intervention programmes using Nirsevimab in England and Wales

IntroductionRespiratory Syncytial Virus (RSV) is a major cause of acute lower respiratory tract infections (ALRI) in infants. There are no licensed vaccines and only one monoclonal antibody available to protect infants from disease. A new and potentially longer-lasting monoclonal antibody, Nirsevimab, showed promising results in phase IIb/III trials. We evaluate the cost-effectiveness of Nirsevimab intervention programmes in England and Wales.MethodsWe used a dynamic model for RSV transmission, calibrated to data from England and Wales. We considered a suite of potential Nirsevimab programmes, including administration to all neonates (year-round); only neonates born during the RSV season (seasonal); or neonates born during the RSV season plus infants less than six months old before the start of the RSV season (seasonal + catch-up).ResultsIf administered seasonally to all infants at birth, we found that Nirsevimab would have to be priced at £63 or less per dose for at least 50% certainty that it could cost-effectively replace the current Palivizumab programme, using an ICER threshold of £20,000/QALY. An extended seasonal programme which includes a pre-season catch-up becomes the optimal strategy at a purchasing price of £32/dose or less for at least 50% certainty. At a purchasing price per dose of £5-32, the annual implementation costs of a seasonal programme could be as high as £2 million before a switch to a year-round strategy would be optimal.DiscussionNirsevimab has the potential to be cost-effective in England and Wales not only for use in high-risk infants.     

Preoperative comprehensive malignancy risk estimation for thyroid nodules: Development and verification of a network-based prediction model

BackgroundIn order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery.MethodsWe retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020).ResultsIn this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAF^V600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively.ConclusionA simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy.     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

医疗领域人工智能应用的研究进展＊

人工智能引发了医疗领域的数字革命，在推动行业发展方面具有极大潜力。本研究围绕临床诊疗、医学研究和公共卫生三个基本场景，聚焦人工智能与传统中医药的交叉与融合，并着重介绍人工智能在疾病诊断、决策支持、医学研究以及重大公共卫生事件中的应用。虽然人工智能在诸多方面显示出独特优势，但仍存在透明度不高、缺乏安全性评估和相关法律法规监管等问题需要谨慎解决，以促进人工智能技术在医疗领域的推广。     

Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review

Objective: To perform a scoping review of the applicability of the Gail model in different countries for different ethnicities. Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and search strategies based on the PICOS approach. The reviewed articles were included if they were published between 2013 and 2018 in English, Portuguese, or Spanish; were original articles available in full online; and described the use of the Gail model. The PubMed, Embase, and Web of Science data bases were searched. Results: A total of 38 articles eligible for analysis were identified, of which 16 used the Gail model to assess breast cancer risk in women, eight analyzed the applicability of this tool in their population, seven compared the tool and/or modified it according to the specific risk factors of their population, and seven cited the model in determining eligibility for chemoprevention. Conclusion: The Gail model has different applicabilities Greater effectiveness and breast cancer risk are found in developed countries.     

骨质疏松症动物模型的构建及实验方法研究进展

骨质疏松症是一种全身性骨代谢病,由多种因素引起,其特点是骨量、骨密度和骨组织的显微结构恶化,骨脆性增强和易发生骨折。骨质疏松症已严重威胁人类健康,最终将导致患者日常活动减少,生活质量降低,死亡率增加。针对不同的研究对象,选择一个正确、理想的动物骨质疏松的模型和实验方法以尽可能再现人类骨质疏松的状态,是开展动物实验的关键。我们综述了当前原发性和继发性骨质疏松等动物模型的构建,以及利用这些模型进行给药实验的研究方法,并对研发治疗骨质疏松的新型药物的选择提供了一些参考建议,以期对后续关于药物的作用机制、研发新药、药物的改良等研究提供思路。     

Prolyl and lysyl hydroxylases in collagen synthesis

Collagens are the most abundant proteins in the extracellular matrix. They provide a framework to build organs and tissues and give structural support to make them resistant to mechanical load and forces. Several intra‐ and extracellular modifications are needed to make functional collagen molecules, intracellular post‐translational modifications of proline and lysine residues having key roles in this. In this article, we provide a review on the enzymes responsible for the proline and lysine modifications, that is collagen prolyl 4‐hydroxylases, 3‐hydroxylases and lysyl hydroxylases, and discuss their biological functions and involvement in diseases.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

Motivations and barriers to pursue cancer genomic testing: A systematic review

ObjectivesSingle-gene testing is associated with psycho-social challenges for cancer patients. Genomic testing may amplify these. The aim of this study was to understand patients’ motivations and barriers to pursue cancer genomic testing, to enable healthcare providers to support their patients throughout the testing process and interpretation of test results.MethodsFive databases were searched for original peer reviewed research articles published between January 2001 and September 2018 addressing motivation for genomic cancer testing. QualSyst was used to assess quality.Results182 studies were identified and 17 were included for review. Studies were heterogenous. Both somatic and germline testing were included, and 14 studies used hypothetical scenarios. 3249 participants were analyzed, aged 18 to 94. Most were female and white. The most common diagnoses were breast, ovarian, lung and colorectal cancer. Interest in testing was high. Motivations included ability to predict cancer risk, inform disease management, benefit families, and understand cancer. Barriers included concerns about cost, privacy/confidentiality, clinical utility, and psychological harm.ConclusionsDespite concerns, consumers are interested in cancer genomic testing if it can provide actionable results for themselves and their families.Practice ImplicationsProviders must manage understanding and expectations of testing and translate genetic information into health-promoting behaviours.