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.     

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.     

A Predictive Model for Reactive Tube Feeding in Head and Neck Cancer Patients Undergoing Definitive (Chemo) Radiotherapy

AimsCareful management of a patient's nutritional status during and after treatment for head and neck squamous cell cancers (HNSCC) is crucial for optimal outcomes. The aim of this study was to develop a model for stratifying a patient's risk of requiring reactive enteral feeding through a nasogastric tube during radiotherapy for HNSCC, based on clinical and treatment-related factors.Materials and methodsA cohort of consecutive patients treated with definitive (chemo)radiotherapy for HNSCC between January 2016 and January 2018 was identified in the institutional electronic database for retrospective analysis. Patients requiring enteral feeding pretreatment were excluded. Clinical and treatment data were obtained from prospectively recorded electronic clinical notes and planning software.ResultsBaseline patient characteristics and tumour-related parameters were captured for 225 patients. Based on the results of the univariate analysis and using a stepwise backwards selection process, clinical and dosimetric variables were selected to optimise a clinically predictive multivariate model, fitted using logistic regression. The parameters found to affect the probability, P, of requiring a nasogastric feeding tube for >4 weeks in our clinical multivariate model were: tumour site, tumour stage (early T0/1/2 stage versus advanced T3/T4 stage), chemotherapy drug (none versus any drug) and mean dose to the contralateral parotid gland. A scoring model using the regression coefficients of the selected variables in the clinical multivariate model achieved an area under the curve (AUC) of 0.745 (95% confidence interval 0.678–0.812), indicating good discriminative performance. Internal validation of the model involved splitting the dataset 80:20 into training and test datasets 10 times and assessing differences in AUC of the model fitted to these.ConclusionsWe developed an easy-to-use prediction model based on both clinical and dosimetric parameters, which, once externally validated, can lead to more personalised treatment planning and inform clinical decision-making on the appropriateness of prophylactic versus reactive enteral feeding.     

青海地区获得性耐药结核病产生的危险因素及其预测模型的构建

目的探讨青海地区原发性与获得性耐药结核病的危险因素及临床发病风险预测模型构建。方法选取2018年7月-2019年7月青海地区132例耐药结核病患者为观察组,青海地区132例非耐药结核病患者为对照组,查阅患者的的临床资料,并自制调查问卷,以问卷调查的方式收集患者的相关信息,分析两组患者的临床指征,采用多因素Logistic回归分析完成风险模型建立,绘制ROC曲线分析风险模型的预测效能。结果单因素结果表明:青海地区原发性与获得性耐药结核病发生率与年龄、性别、与患者接触、3月底痰涂片结果无统计学意义(P>0.05);与居住地、婚姻状况、家庭收入、治疗末痰涂片结果、体重指数、抗结核药物性肝损害、结核病灶数、结核空洞、合并糖尿病及登记分类初治具有统计学意义(P<0.05);多因素logistic结果表明:青海地区原发性与获得性耐药结核病发生率与居住地、登记分类、体重指数、抗结核药物性肝损害、3月末肺结核病灶数及痰涂片结果,具有统计学意义(P<0.05);ROC曲线结果表明:构建获得性耐药结核病发病风险预测模型用于青海地区原发性与获得性耐药结核病患者中ACU值为0.847,预测敏感性为87.46%,特异性为90.29%。结论青海地区原发性与获得性耐药结核病危险因素较多,不同因素能相互作用、相互影响,构建获得性耐药结核病发病风险预测模型,能较好的预测临床发病,有助于指导临床诊疗。