基于季节性ARIMA模型的新疆肺结核发病预测分析

目的 探讨季节性时间序列模型(autoregressive integrated moving average,ARIMA)在新疆肺结核发病预测中的应用,并验证模型的可行性和适用性。 方法 采用季节性ARIMA(p, d, q )(P, D, Q)_s拟合2005年1月—2019年8月新疆地区肺结核月发病人数,建立多个季节时间序列模型并进行比较,选出最优模型对2019年9—12月肺结核发病人数进行预测。 结果 2005年1月—2019年8月新疆地区肺结核累积发病人数为627 869例,年平均发病人数为3 567例。 新疆地区肺结核月发病数具有季节性,1—5月平均发病数高于平均水平,6—12月平均发病数低于平均水平,发病高峰为1月和3月,发病低谷为9月。通过赤池信息量(Akaike Information Criterion,AIC)和贝叶斯信息量(Bayesian Information Criterion,BIC)最小原则得出,ARIMA(1, 1, 1 )(0, 1, 2)₁₂是最优模型,其残差序列为白噪声,参数的回归系数均具有统计学意义,拟合的平均绝对百分比误差MAPE为8.723%。预测的MAPE为18.674%,真实值均处于预测值的95%置信区间内。 结论 ARIMA(1, 1, 1 )(0, 1, 2)₁₂模型能够较好地拟合新疆肺结核发病数据,并进行短期预测,对新疆卫生防控措施的制定具有一定指导意义。     

基于CiteSpace的我国出院准备度研究可视化分析

目的分析我国出院准备度的研究现况、热点及前沿,为出院准备相关工作及研究方向提供依据。方法以中国知网（CNKI）为检索源,以“出院准备度”“出院准备”“出院计划”为检索词,检索1982年1月1日—2020年12月31日相关文献,应用CiteSpace V5.6.R5软件进行可视化分析。结果共纳入相关文献820篇。年发文量呈逐年上升趋势。发文机构和作者分布均较为分散,且相互合作不紧密。研究热点有出院计划、影响因素、护理和出院准备服务等,研究前沿为延续护理和脑卒中。结论出院准备度日益受到关注,研究机构、作者间需加强合作,不断拓展研究内容的广度和深度。     

2003—2018年间中国女性宫颈癌发病与死亡趋势研究

  目的  研究2003—2018年中国20~79岁女性宫颈癌发病率和死亡率的变化趋势,对未来五年宫颈癌发病及死亡率的趋势进行预测。  方法  收集我国2003—2018年20~79岁女性宫颈癌的发病和死亡数据,利用联结点回归模型分析趋势变化规律,进一步利用年龄-时期-队列模型探讨年龄、时期和队列因素对宫颈癌发病和死亡率的影响。分别建立自回归滑动平均混合模型(autoregressive integrated moving average model, ARIMA)、灰色模型(grey model, GM)(1,1)和误逆差传播(back propagation, BP)神经网络模型对发病率和死亡率进行拟合,选取预测精度高的模型预测未来五年宫颈癌的发病率和死亡率。  结果  2003—2018年间女性宫颈癌的发病率具有2个转折点,发病趋势先快速上升随后下降;死亡率具有1个转折点,趋势是先下降再上升。总体上看,宫颈癌的发生风险随着年龄的增长而增大,在55~＜60岁达到峰值后缓慢下降。死亡风险从年龄上看不断上升,时期效应随着时期的推进而增大,队列效应则不断减弱。通过对比发现BP神经网络模型拟合的效果较好。  结论  2003—2018年间中国女性宫颈癌的发病率和死亡率整体上呈现下降的趋势,受年龄影响较大而受时期和队列的影响较小,未来五年发病率和死亡率将呈下降趋势。因此,应加强女性宫颈癌筛查和HPV疫苗接种工作,做好防控措施。     

我国常见细菌耐药趋势预测研究:基于灰色GM (1,1)模型

 目的 通过对耐甲氧西林金黄色葡萄球菌（MRSA）、耐碳青霉烯类铜绿假单胞菌（CRPA）、耐碳青霉烯类鲍曼不动杆菌（CRAB）、耐第三代头孢菌素的大肠埃希菌（3GCR-E.coli）、耐第三代头孢菌素的肺炎克雷伯菌（3GCR-KP）等细菌耐药数据构建灰色预测模型,分析细菌耐药特征的变化趋势,探讨灰色预测模型在细菌耐药领域的应用价值。方法 采用2014-2018年全国细菌耐药监测报告中MRSA、CRPA和CRAB、3GCR-E.coli、3GCR-KP等耐药率数据构建灰色预测GM （1,1）模型。用后验差比C值和小误差概率P值评估模型精度,用相对误差和级比偏差评估模型拟合效果,并用2019-2020年数据对模型预测效果进行验证。最终根据模型对2021-2023年的耐药率进行预测。结果 本研究构建的GM （1,1）模型对MRSA、CRPA、CRAB、3GCR-E.coli和3GCR-KP等细菌耐药率预测效果较好,根据该模型预测到2023年其耐药率分别可降低至23.9%、15.2%、50.2%、43.8%、26.1%。结论 全国针对细菌耐药情况采取的控制措施取得明显成效,GM （1,1）模型对细菌耐药率预测效果较好,可在细菌耐药管理领域推广应用。     

预测加氢裂化过程的产率

以缓和加氢裂化数据为基础,对于两种典型的加氢裂化动力学模型－－Stangeland模型和改进MHC模型,使用Shor最优化法进行了参数的拟合,比较了这两种动力学模型的结果、算法、复杂度以及预测能力。结果表明,改进MHC模型是一种更为合理的动力学模型,该模型也可用于实际加氢过程。     

Sperm binding to hyaluronan is an excellent predictor of Nili-Ravi buffalo bull fertility

This study reports the first evaluation of sperm hyaluronan binding assay (HBA) for predicting the fertility of Nili-Ravi buffalo bulls in relation to standard parameters of sperm quality. Cryopreserved semen doses of low (n = 6), medium (n = 3) and high fertility (n = 8) bulls based on their respective return rates were used. Significantly, more spermatozoa bound to hyaluronan from the most fertile bulls (57.15% ± 1.44) compared with medium (42.46% ± 1.08) and low fertility bulls (29.70% ± 0.78). A strongly positive correlation (r = .824, p < .01) was found between HBA and fertility that predicts a 67.9% variability (r² = .679, p < .01) in fertility. HBA was also strongly positively correlated with sperm viability (r = .679, p < .01) followed by their live/dead ratio (r = .637, p < .01), uncapacitated spermatozoa (r = .631, p < .01), normal apical ridge (r = .459, p < .01), motility (r = .434, p < .01), mature spermatozoa with low residual histones (r = .364, p < .01), high plasma membrane integrity (r = .316, p < .01) and nonfragmented DNA levels (r = .236, p < .05). It was negatively correlated with spermatozoa having reacted acrosome (r = −.654, p < .01). A fertility model built using a combination of sperm HBA and either sperm livability or viability predicts, respectively, 86.1% (r² = .861, p < .01) and 85.9% (r² = .859, p < .01) variability in buffalo bull fertility. In conclusion, sperm HBA may prove to be a single robust predictor of Nili-Ravi buffalo bull fertility.     

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BackgroundApproximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA.MethodsPatients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined.ResultsThe most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes.ConclusionModels produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction.Level of EvidenceLevel III, diagnostic study.     

Is Patient Satisfaction After Total Knee Arthroplasty Predictable Using Patient Characteristics and Preoperative Patient-Reported Outcomes?

BackgroundDissatisfaction after total knee arthroplasty (TKA) remains a difficult problem. Patient characteristics and preoperative patient-reported outcomes (PROs) are potential predictors of satisfaction one year after TKA. Being able to predict the outcome preoperatively might reduce the number of less satisfied patients.MethodsA retrospective cohort study on prospectively collected data of 1239 primary TKA patients (ASA I-II, BMI <35) was performed. Primary outcome was degree of patient satisfaction one year after TKA (Numeric Rating Scale (NRS) 0-10). Secondary outcomes were degree of patient satisfaction six months and two years after TKA and being dissatisfied (NRS 0-6) or satisfied (NRS 7-10) at all three time points. Multivariate linear and binary logistic regression analyses were executed with patient characteristics and preoperative PROs as potential predictors.ResultsOne year after TKA, median NRS satisfaction score was 9.0 (8.0-10.0) and 1117 (90.2%) patients were satisfied. BMI, degree of medial cartilage damage, previous knee surgery, Knee injury and Osteoarthritis Outcome Score-Physical Function Short Form score, EQ VAS score, and anxiety were identified as predictors of the degree of patient satisfaction (P = .000, R² = 0.027). Models on secondary outcomes reported R² of 1.7%-7.1% (P < .05). All models showed bad agreement between observed and predicted values for lower NRS satisfaction scores and being dissatisfied.ConclusionThe degree of patient satisfaction and the chance of being dissatisfied or satisfied six months, one, and two years after TKA are predictable by patient characteristics and preoperative PROs but not at a reliability level that is clinically useful.     

基于近、中红外光谱技术的热毒宁注射液制备过程中金银花浓缩过程含量预测研究

目的 采用近红外光谱（near infrared spectroscopy,NIRS）、中红外光谱（mid-infrared spectroscopy,MIRS）技术实现对热毒宁注射液制备过程中金银花浓缩过程绿原酸、新绿原酸、隐绿原酸、异绿原酸A、异绿原酸B、异绿原酸C、断氧化马钱子苷和固含量8个质控指标的含量预测,并对比2种技术的预测效果。方法 收集热毒宁注射液制备过程中金银花浓缩过程样本,进行NIRS、MIRS采集和含量测定,优选最佳光谱预处理方法和特征波段,采用偏最小二乘（partial least square,PLS）法建立8个质控指标的含量预测模型,并比较8个质控指标的NIRS、MIRS模型性能,得到8个最优含量预测模型,并对其进行外部验证。结果 NIRS技术对绿原酸、隐绿原酸、异绿原酸C、断氧化马钱子苷、固含量的预测效果更好,平均相对预测误差（average relative prediction error,ARPE）分别为1.57%、1.88%、4.13%、3.79%、0.94%,故选用NIRS模型作为这5个质控指标的最佳模型;MIRS技术对新绿原酸、异绿原酸A、异绿...     

基于生物信息学探讨SARS-CoV-2介导免疫炎症反应诱发动脉粥样硬化进展的潜在机制与防治中药筛选

目的 应用生物信息学技术,从免疫炎症角度探索严重急性呼吸综合征冠状病毒-2(severe acute respiratory syndrome coronavirus-2,SARS-CoV-2)感染诱导动脉粥样硬化（atherosclerosis,AS）进展的核心靶点及重要通路,进而预测潜在防治中药。方法 从基因表达数据库（Gene Expression Omnibus,GEO）中获取新型冠状病毒肺炎患者和动脉粥样硬化患者芯片数据,利用“limmar”包及“Venn”包筛选2种疾病的共同差异表达基因（differentially expressed genes,DEGs）,对共同DEGs进行基因本体论（gene ontology,GO）和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes,KEGG）分析,注释其功能及重要通路。对2组基因集进行免疫细胞和免疫功能打分,评估免疫细胞浸润水平。利用STRING数据库,构建蛋白-蛋白互作（protein-protein interaction,PPI）网络;通过Cytoscape的CytoH...