How can we evaluate an interrelation of symptoms?

A pandemic of 2019 novel coronavirus (COVID-19) is an international problem and factors associated with increased risk of mortality have been reported. However, there exists limited statistical method to estimate a comprehensive risk for a case in which a patient has several characteristics and symptoms concurrently. We applied Boolean Monte Carlo method (BMCM) to the Novel Corona Virus 2019 Dataset to determine interrelation of patient’s characteristics and symptoms. In the analyses, age, fever as an onset symptom, and sex were used as explanatory variables, and death as the objective variable. Among 265 patients included in the analysis, the interrelations for estimating death were determined as age “and” fever “and” sex (p < 0.0001 for both operators). This result indicates that satisfying the three conditions of age, fever, and sex concurrently may be associated with an increased risk of mortality.     

Are nontreponemal tests suitable for monitoring syphilis treatment efficacy? Evidence from rabbit infection models

ObjectivesWe aimed to characterize kinetics of non-treponamal antibody titres during the natural course of syphilis and explore their roles in monitoring syphilis treatment efficacy.MethodsSixty New Zealand white male rabbits were challenged with Nichols or Amoy Treponema pallidum strains, and the rapid plasma reagin (RPR) test was performed to quantify non-treponemal antibody titres during the infection course. Viable T. pallidum in the challenged rabbits was assessed with rabbit infectivity tests.ResultsThe RPR titres of the Nichols or Amoy strain between no benzathine penicillin G (BPG) and BPG treatment subgroups displayed a similar trend: first ascending and then descending. Compared with baseline, the proportions of fourfold decline in RPR titres in the Nichols or Amoy group presented a similar result on days 30, 60 and 180 between the no BPG and BPG treatment subgroups (0%, 0/5; 80%, 4/5; 100%, 5/5; vs. 0%, 0/5; 80%, 4/5; 100%, 5/5; p 0.999; 0%, 0/5; 80%, 4/5; 80%, 4/5; vs. 40%, 2/5; 100%, 5/5; 100%, 5/5; p 0.098, respectively). Compared with the maximum baseline titre, the proportion of fourfold decline in PRR titre also showed a similar result in the two groups on days 30, 60 and 180 between the no BPG and the BPG treatment subgroups (0%, 0/5; 100%, 5/5; 100%, 5/5, vs. 40%, 2/5; 100%, 5/5; 100%, 5/5; p 0.129; 0%, 0/5; 100%, 5/5; 100%, 5/5, vs. 80%, 4/5; 100%, 5/5; 100%, 5/5; p 0.091, respectively. Moreover, regardless of whether the RPR titres presented a fourfold decline, viable T. pallidum could be detected in untreated rabbits' lymph nodes at 30, 60 and 180 days post infection, while viable T. pallidum was not detected in any of the treated rabbits' lymph nodes.ConclusionsThe RPR titre increased and then decreased (even became negative) during the natural course of syphilis, similar to that seen after BPG treatment. The RPR tetre is thus a questionable indicator of syphilis treatment efficacy.     

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii

ObjectivesIncreased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.MethodsA. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters.ResultsMonotherapies resulted in regrowth to ~10¹⁰ cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC₅₀ (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 10⁸ cfu/mL starting inoculum to a point of 10⁰ cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be ‘loaded’ with 80.5% and 42.2% of the daily dose, respectively).ConclusionThis study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the ‘traditional’ indices of antibiotic action.     

基于超声影像组学的乳腺癌预测模型的诊断性能研究

目的评估并比较基于不同机器学习算法建立的乳腺癌超声影像组学预测模型的诊断性能。 方法回顾性收集2017年1月至2019年4月就诊皖南医学院第一附属医院、有明确病理结果的乳腺肿块病例828例，以2018年8月31日为节点将其分为训练集（526例）和验证集（302例），提取肿块的超声影像组学特征并进行特征筛选，运用k最近邻（kNN）、逻辑回归（LR）、朴素贝叶斯（NB）、随机森林（RF）和支持向量机（SVM）5种机器学习算法分别建立预测模型，使用重复交叉验证方法做内部验证，计算比较各模型的敏感度、特异度、阳性预测值（PPV）和阴性预测值（NPV），并实施外部验证，绘制ROC曲线并比较ROC曲线下面积（AUC）以评价模型的鉴别诊断性能，绘制校准曲线评价模型校准度。 结果从提取的109个影像组学特征中筛选出19个特征建立了5种机器学习算法模型。在内部验证中，5种模型的敏感度、特异度、PPV、NPV比较，总体差异均有统计学意义（P均＜0.001）。LR模型的特异度、PPV、NPV中位数分别为0.769、0.816、0.778，3项指标均高于其他4种模型；敏感度中位数为0.824，高于kNN、RF和SVM模型。此外，SVM模型的特异度、PPV、NPV中位数分别为0.706、0.774、0.759，虽均低于LR模型，但均高于其他3种模型。在外部验证中，LR、SVM、RF、kNN和NB的AUC依次为0.890、0.832、0.821、0.746和0.703，其中LR与SVM的AUC差异有统计学意义（P=0.012）；此外，各模型在校准性能上表现并不一致，LR和SVM模型的校准曲线显示乳腺癌实际概率与预测概率之间的一致性较好。 结论以超声影像组学特征为基础，运用不同机器学习算法建立的乳腺癌超声预测模型，均表现出较高的诊断性能，其中LR模型表现最为突出；选择合适的机器学习算法有助于进一步提高预测模型的诊断性能，提供更加准确的量化预测结果。     

Nomogram Predicting the Risk of Locoregional Recurrence After Mastectomy for Invasive Micropapillary Carcinoma of the Breast

BackgroundThe risk of locoregional recurrence (LRR) after mastectomy for breast invasive micropapillary carcinoma (IMPC) remains poorly defined. We aimed to construct an effective prognostic nomogram to estimate the individualized risk of LRR for providing accurate information for long-term follow-up.Patients and MethodsA total of 388 patients with breast IMPC were included in the current study. Based on the Cox regression and clinical significance, a nomogram with an online prediction version was created. This model was evaluated and internally validated by concordance index and calibration plot. Receiver operating characteristic curve and decision curve analysis were used to assess the discrimination and clinical utility, and Kaplan-Meier curves estimated the probability of LRR.ResultsThe variables (age, lymph node metastasis, hormone receptor status, lymphovascular invasion, histologic grade, and adjuvant radiotherapy) were included in the nomogram. This model was well-calibrated to predict the possibility of LRR and displayed favorable clinical utility; the concordance index was 0.86 (95% confidence interval, 0.81-0.91), which was higher than any single predictor. The area under the curve of the nomogram was 0.89, whereas that of the conventional staging system was 0.72. An online prognostic nomogram was built for convenient use. Kaplan-Meier curves showed that the nomogram had a better risk stratification than the conventional staging system.ConclusionsThe nomogram could accurately predict the individualized risk of LRR after mastectomy for breast IMPC. By identifying the risk stratification, this model is expected to assist clinicians and patients in improving long-term follow-up strategies.     

广东省3个城市大气污染物二氧化氮对居民的死亡效应研究

目的 探讨广东省广州、佛山及珠海3个城市的大气污染二氧化氮（NO₂）对居民每日死亡效应的影响。方法 收集2013—2016年广州、佛山及珠海3个城市的每日大气污染物浓度、气象资料数据和居民的每日总死亡数据,对数据基本特征进行统计描述,并通过Spearman分析其相关关系,最后利用广义相加模型（GAM）分别对3个城市的NO₂日均浓度及每日总死亡数据进行分析。结果 2013—2016年广州、佛山及珠海市的大气污染物NO₂日均浓度分别为46.4、48.4、33.1 μg/m³,均符合国家二级标准（80 μg/m³）。广州市大气中NO₂日均浓度对当天、滞后1、2 d的每日总死亡人数、循环系统疾病死亡人数的影响有统计学意义（均P<0.05）,佛山市滞后1、2 d的NO₂日均浓度对居民每日总死亡人数及循环系统疾病每日死亡人数的影响有统计学意义（均P<0.05）,广州和佛山市均表现出滞后1 d时效应最大。滞后2 d的NO₂日均浓度对广州市居民的呼吸系统疾病每日死亡人数有影响（ER=1.38）。结论 大气污染物NO₂浓度的上升会引起居民死亡风险的增加,应引起重视。     

Developmental trajectory of purpose identification during adolescence: Links to life satisfaction and depressive symptoms

IntroductionWhereas evidence has shown that a sense of purpose is linked to optimal adjustment, longitudinal work investigating the development of purpose identification as well as its effect on psychological functioning among non-Western samples during adolescence is needed.MethodsThree hundred and eighty-seven senior high school students (253 female, 65.37%; mean age = 15.76 years at the first investigation) from Taiwan completed surveys four times beginning in the fall of tenth grade and ending in the spring of eleventh grade with a six-month interval. Using self-ratings, purpose identification was evaluated in all four assessments and psychological functioning was examined through life satisfaction and depressive symptoms in the first and the last survey.ResultsGrowth curve analyses revealed an increased slope in purpose identification over the first two years of high school, and such a trajectory was similar across boys and girls. Additionally, increases in purpose identification predicted enhanced life satisfaction and reduced depressive symptoms among both boys and girls. There was only one gender difference: The negative association between purpose identification trajectory and depressive symptoms was stronger for girls than for boys.ConclusionsThere is an increase in the development of identified purpose during middle adolescence among high school students in Taiwan. Such change not only promotes life satisfaction in adolescents but is also preventive of adolescent depression. As such, the current findings highlight the significance for adolescents to discover and commit to a purpose.     

商业医疗保险参保的供给影响因素研究——基于CGSS2013数据的实证分析

运用Prebit模型分析CGSS2013家户微观数据和保险供给宏观数据,探究影响商业医疗保险参保的因素,发现供给不足制约居民参保,对西部、中部和农村地区影响显著。家庭财富不足、保险知识缺乏、基本医疗保险也影响居民参保。主张推动保险供给侧结构性改革,保险公司增设分支机构,补齐供给短板。政府增加农民收入,普及保险知识,提供有限保障。     

BOPPPS教学模型在推拿学教学中的实践和探索——以[扌衮]法实训为例

BOPPPS教学模型是以建构主义和交际法为理论依据的教学设计模型,学生积极的参与式互动和教师的及时评价反馈,促成高效的课程教学。本次研究以推拿学中的代表性手法——[扌衮]法实训教学为例,探讨BOPPPS教学模型在推拿学课程的有效构建,使学生能更好地掌握[扌衮]法的理论和实践操作,进一步优化推拿学的教学设计。