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。     

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。     

Adverse Effects Associated with Multiple Categories of Dietary Supplements: The Military Dietary Supplement Use Study

BackgroundAbout 50% of Americans and 70% of US military service members (SMs) regularly use dietary supplements (DSs) and some are associated with adverse effects (AEs). SMs are more likely to use unsafe DSs than civilians.ObjectiveThe aim of this investigation was to examine the prevalence of, and factors associated with, AEs.DesignCross-sectional.ParticipantsA stratified random sample of 200,000 US SMs from the Air Force, Army, Marine Corps, and Navy were obtained from military workforce records. Eighteen percent (n = 26,681) of successfully contacted SMs (n = 146,365) volunteered to participate between December 2018 and August 2019. Participants completed a detailed online questionnaire on demographic characteristics, lifestyle factors, and AEs associated with DS use.Main outcome measurePrevalence of, and factors associated with, AEs among DS users.Statistical analysisPrevalence of AEs was calculated by DS categories. Linear trends, χ² statistics, and multivariable logistic regression examined associations between AEs and demographic characteristics, lifestyle factors, and number DSs consumed.ResultsProportion of DS users (≥ 1 time /week) reporting ≥1 AE was 18% overall, 20% for combination products (ie, weight loss, muscle building, and before/after workout supplements), 8% for purported prohormones, 6% for protein/amino acid products, 6% for multivitamin/multiminerals, 6% for individual vitamins/minerals, 4% for herbal products, and 2% for joint health products. Combination products are very popular in military personnel with nearly half of SMs regularly taking them. In multivariable analysis, reporting AEs were independently associated with female gender, younger age, higher body mass index, smoking, higher alcohol intake, service in the Army, Navy, or Marine Corps (compared with Air Force), and consumption of a greater number of DSs.ConclusionsA large proportion of SMs report experiencing AEs, especially users of combination products and purported prohormone supplements. This study presents contemporary data collected from a very large at-risk population on potentially hazardous categories of DSs.     

中西医结合治疗眼底病的实验和临床研究

眼底病是一种复杂的致盲率很高的眼部疾病 ,包括视网膜脱离、视网膜玻璃体出血和增殖性玻璃体视网膜病变 (PVR)等疾病。本文采用中西医结合的治疗方法对动物模型和 1998～ 2 0 0 0年此类眼病患者进行研究 ,疗效显著 ,使眼底病的治愈率有了很大幅度的提高。中医药对眼底病的治疗常用活血化瘀复方治疗 ,对于从动物实验及临床两方面同时进行中西医结合的系统治疗研究 ,尚未见有报道。     

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.     

基于中西医临床病证特点的系统性红斑狼疮动物模型研究评述

基于系统性红斑狼疮（SLE）的中西医临床病证特点,参考国内外SLE动物模型研究文献,依据临床诊断标准,对现有模型的临床符合度进行评价,经对比分析发现,自发小鼠模型中的MRL/lpr小鼠模型与诱导模型中的姥鲛烷诱导模型中西医临床病证吻合度最高。但目前SLE动物模型多按照西医病因特点建模,缺乏中医病证结合模型,中医符合度低,因此在现有研究基础上增加中医干扰因素,彰显中医辨证优势,加强SLE中医辨证相关研究是今后努力的方向。