血清IL-17、IL-32、IL-33、IL-37联合检测对初治多发性骨髓瘤患者早期治疗反应性的预测价值

摘要 目的：探讨血清白细胞介素-17（IL-17）、白细胞介素-32（IL-32）、白细胞介素-33（IL-33）、白细胞介素-37（IL-37）联合检测对接受硼替佐米为基础一线治疗方案的初治多发性骨髓瘤（MM）患者早期治疗反应性的预测价值。方法：选择2018年7月至2021年3月期间陕西省人民医院收治的初治MM患者176例为研究对象,所有患者均接受以硼替佐米为基础一线的治疗方案,根据早期治疗反应性分为敏感组（142例）和非敏感组（34例）;采用酶联免疫吸附法检测并比较两组血清IL-17、IL-32、IL-33、IL-37水平,并分析其联合检测对早期治疗反应性的预测价值。结果：敏感组治疗前血清IL-17、IL-32水平低于非敏感组,IL-33、IL-37水平高于非敏感组（P＜0.05）。多因素logistic回归分析显示,年龄≥65岁、血清IL-17≥29.70 pg/mL、IL-32≥63.02 ng/L、肿瘤分期III期是早期治疗反应性的危险因素（P＜0.05）,IL-33＞141.97 pg/mL、IL-37＞69.17 ng/L是保护因素（P＜0.05）。血清IL-17、IL-32、IL-33、IL-37联合检测预测早期治疗反应性的曲线下面积（AUC）为0.866（95%CI：0.801~0.972）。结论：年龄、肿瘤分期、血清IL-17、IL-32、IL-33、IL-37是MM患者早期治疗反应性的影响因素,联合检测血清IL-17、IL-32、IL-33、IL-37水平对接受硼替佐米为基础一线治疗方案的初治MM患者早期治疗反应性预测价值较高。     

术前预后营养指数和血清转铁蛋白与老年髋部骨折患者术后切口愈合的关系及其预测价值分析

摘要 目的：研究术前预后营养指数（PNI）和血清转铁蛋白（TRF）与老年髋部骨折（HF）患者术后切口愈合不良（PWH）的关系及其预测价值。方法：选取2020年1月～2022年3月南京市中医院收治的252例接受手术治疗老年HF患者,根据术后切口愈合情况分为PWH组（n=27）和非PWH组（n=225）。收集患者基础资料、术前PNI和血清TRF水平。采用多因素Logistic回归分析老年HF患者术后PWH的影响因素,受试者工作特征（ROC）曲线分析PNI和血清TRF水平对老年HF患者术后PWH的预测价值。结果：252例老年HF患者术后出现27例PWH,其中24例切口长时间不愈合,3例切口裂开。与非PWH组比较,PWH组体质量指数（BMI）和白蛋白、淋巴细胞计数（LC）、PNI、血清TRF水平更低,糖尿病比例和术中出血量更高（P＜0.05）。多因素Logistic回归分析显示,BMI≥18.5 kg/m²（OR=0.648,95％CI：0.457～0.919）、PNI（OR=0.954,95％CI：0.932～0.976）、血清TRF（OR=0.484,95％CI：0.307～0.761）升高是老年HF患者术后PWH的保护因素,糖尿病（OR=2.651,95％CI：1.182～5.948）、术中出血量增加（OR=1.013,95％CI：1.005～1.021）是危险因素（P＜0.05）。ROC曲线分析显示,PNI和血清TRF水平单独与联合预测老年HF患者术后PWH的曲线下面积（AUC）分别为0.808、0.770、0.871,灵敏度分别为70.37％、55.56％、92.59％,特异度分别为80.65％、85.81％、70.32％。二者联合预测老年HF患者术后PWH的AUC大于二者单独预测（P＜0.05）。结论：术前PNI和血清TRF水平降低是老年HF患者术后PWH的危险因素,二者联合对老年HF患者术后PWH的预测价值较高。     

Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.     

The Effect of Recombination on the Accuracy of Phylogeny Estimation

Phylogenetic studies based on DNA sequences typically ignore the potential occurrence of recombination, which may produce different alignment regions with different evolutionary histories. Traditional phylogenetic methods assume that a single history underlies the data. If recombination is present, can we expect the inferred phylogeny to represent any of the underlying evolutionary histories? We examined this question by applying traditional phylogenetic reconstruction methods to simulated recombinant sequence alignments. The effect of recombination on phylogeny estimation depended on the relatedness of the sequences involved in the recombinational event and on the extent of the different regions with different phylogenetic histories. Given the topologies examined here, when the recombinational event was ancient, or when recombination occurred between closely related taxa, one of the two phylogenies underlying the data was generally inferred. In this scenario, the evolutionary history corresponding to the majority of the positions in the alignment was generally recovered. Very different results were obtained when recombination occurred recently among divergent taxa. In this case, when the recombinational breakpoint divided the alignment in two regions of similar length, a phylogeny that was different from any of the true phylogenies underlying the data was inferred.     

Large-sliding contact elements accurately predict levels of bone-implant micromotion relevant to osseointegration

Primary stability is recognised as an important determinant in the aseptic loosening failure process of cementless implants. An accurate evaluation of the bone–implant relative micromotion is becoming important both in pre-clinical and clinical studies. If the biological threshold for micro-movements is in the range 100–200 μm then, in order to be discriminative, any method used to evaluate the primary stability should have an accuracy of 10–20 μm or better. Additionally, such method should also be able to report the relative micromotion at each point of the interface. None of the available experimental methods satisfies both requirements. Aim of the present study is to verify if any of the current finite element modelling techniques is sufficiently accurate in predicting the primary stability of a cementless prosthesis to be used to decide whether the micromotion may or may not jeopardise the implant osseointegration. The primary stability of an anatomic cementless stem, as measured in vitro, was used as a benchmark problem to comparatively evaluate different contact modelling techniques. Frictionless contact, frictional contact and press-fitted frictional contact conditions were modelled using alternatively node-to-node, node-to-face and face-to-face contact elements. The model based on face-to-face contact elements accounting for frictional contact and initial press-fit was able to predict the micromotion measured experimentally with an average (RMS) error of 10 μm and a peak error of 14 μm. All the other models presented errors higher than 20 μm assumed in the present study as an accuracy threshold.     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

Indicator species for avian biodiversity hotspots: Combination of specialists and generalists is necessary in less natural environments

In this work, I tested the premise that the distribution of a group of few common bird species can be used to predict bird species hotspots in Central Italy. The data on bird observations were collected on 530 sampled sites (150 in cultivated, 150 in forest, 150 in grassland and 80 in urban and peri-urban environments). In each environment, sampled sites with values of bird species richness in the upper than third quartile were classified as high species richness spots (HSRS), while sites with lower bird species richness were classified as non-HSRS (binary classification system).Generalized Linear Models (GLM) were applied using HSRS or non-HSRS as binomial response variable and bird species occurrence was used as the predictor variable. All selected models showed “fair” or “good” capacities to predict the avian hotspots, using only few common birds (4–6) species. However, bird species selected as predictors were different on each environment. In more natural environments (grassland, forest), specialist species were selected, while in most disturbed environments (cultivated and urban) both generalist and specialist species were selected. The results are in agreement with other studies which show how homogenization of bird communities is strongly correlated to landscape disturbance. The findings supports the hypothesis that indicators have to incorporate both specialists and generalist’s species simultaneously. Furthermore, the groups of birds selected as surrogates are easy to detect and this makes it possible to involve citizen-science programmes in obtain data. This approach can be a cheap and efficient and can help to significantly speed up the process of assessing ecosystems that might be under threat.