Effects of ethanol on murine aggression assessed by biting of an inanimate target

The effects of acutely administered ethanol (0, 0.5, 1.0 and 2.0 g/kg, IP) were studied in a tube-restraint/target biting model of aggressive responding using naive group-and individually-housed male Swiss mice. Behavioural measures were the latency to the first bite and the biting frequency. In saline-injected control animals, the levels of responding were significantly higher in group-housed than isolated mice. Animals given alcohol exhibited a dose-dependent suppression of biting frequency, and an increase in biting latency. Mice experienced in the tube-testing situation showed reduced baseline levels of biting, but alcohol produced similar effects to those in naive mice. There was no evidence of a biphasic action of alcohol.     

妊娠期高血压患者妊娠结局调查及相关影响因素分析

目的 调查温州市人民医院2018—2021年妊娠期高血压疾病(hypertensive disorders of pregnancy,HDP)患者妊娠结局,并分析其妊娠结局的相关影响因素,为临床采取对应干预措施、降低不良妊娠结局发生风险提供参考。 方法 选取2018年1月—2021年12月于温州市人民医院分娩的400例HDP患者(单胎妊娠)的临床资料,开展回顾性分析,根据其妊娠结局分为不良妊娠结局组(n=157)与正常妊娠结局组(n=243)。比较两组临床资料,分析HDP患者不良妊娠结局发生的影响因素,构建logistic回归模型方程,并分析logistic回归模型的预测价值。 结果 400例HDP患者中共157例(39.25%)发生不良妊娠结局;单因素分析显示患者年龄、孕前BMI、分娩方式、妊娠期糖尿病(gestational diabetes mellitus,GDM)、负性情绪与不良妊娠结局的发生有关(P＜0.05);logistic回归模型显示,年龄≥35岁(OR=23.815,95%CI:10.370～54.655)、孕前BMI≥24.0(OR=16.010,95%CI:6.832～34.620)、阴道分娩(OR=16.336,95%CI:7.325～36.403)、GDM(OR=26.337,95%CI:11.908～58.253)、负性情绪(OR=20.682,95%CI:2.791～54.876)均为HDP患者不良妊娠结局发生的独立危险因素(P＜0.05);5个独立危险因素构建logistic回归模型方程为logistic(P)=-4.125+年龄×3.170+孕前BMI×2.773+阴道分娩×2.793+GDM×3.271+负性情绪×3.029;当logistic(P)=4.11,预测HDP患者发生不良妊娠结局的曲线下面积为0.899(95%CI:0.865～0.926),预测敏感度为84.36%,特异度为82.17%;根据设定的评分标准与不良妊娠结局发生情况,可将HDP患者划分为低风险(0～4分)、中风险(5～8分)与高风险(9～12分)。 结论 HDP患者不良妊娠结局发生率较高,年龄、孕前BMI、阴道分娩、GDM、负性情绪均为不良妊娠结局发生的影响因素,构建logistic回归模型可预测不良妊娠结局发生风险,有助于临床制定相关干预措施。     

The Role of Maternal Weight in the Hierarchy of Macrosomia Predictors; Overall Effect of Analysis of Three Prediction Indicators

So far it has not been established which maternal features play the most important role in newborn macrosomia. The aim of this study is to provide assessment of a hierarchy of twenty six (26) maternal characteristics in macrosomia prediction. A Polish prospective cohort of women with singleton pregnancy (N = 912) which was recruited in the years 2015–2016 has been studied. Two analyses were performed: for probability of macrosomia > 4000 g (n = 97) (vs. 755 newborns 2500–4000 g); and for birthweight > 90th percentile (n = 99) (vs. 741 newborns 10–90th percentile). A multiple logistic regression was used (with 95% confidence intervals (CI)). A hierarchy of significance of potential predictors was established after summing up of three prediction indicators (NRI, IDI and AUC) calculated for the basic prediction model (maternal age + parity) extended with one (test) predictor. ‘Net reclassification improvement’ (NRI) focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including the results for healthy and ill women. ‘Integrated discrimination improvement’ (IDI) shows the difference between the value of mean change in predicted probability between the group of ill and healthy women when a new factor is added to the model. The area under curve (AUC) is a commonly used indicator. Results. The macrosomia risk was the highest for prior macrosomia (AOR = 7.53, 95%CI: 3.15–18.00, p < 0.001). A few maternal characteristics were associated with more than three times higher macrosomia odds ratios, e.g., maternal obesity and gestational age ≥ 38 weeks. A different hierarchy was shown by the prediction study. Compared to the basic prediction model (AUC = 0.564 (0.501–0.627), p = 0.04), AUC increased most when pre-pregnancy weight (kg) was added to the base model (AUC = 0.706 (0.649–0.764), p < 0.001). The values of IDI and NRI were also the highest for the model with maternal weight (IDI = 0.061 (0.039–0.083), p < 0.001), and (NRI = 0.538 (0.33–0.746), p < 0.001). Adding another factor to the base model was connected with significantly weaker prediction, e.g., for gestational age ≥ 38 weeks (AUC = 0.602 (0.543–0.662), p = 0.001), (IDI = 0.009 (0.004; 0.013), p < 0.001), and (NRI = 0.155 (0.073; 0.237), p < 0.001). After summing up the effects of NRI, IDI and AUC, the probability of macrosomia was most strongly improved (in order) by: pre-pregnancy weight, body mass index (BMI), excessive gestational weight gain (GWG) and BMI ≥ 25 kg/m². Maternal height, prior macrosomia, fetal sex-son, and gestational diabetes mellitus (GDM) occupied an intermediate place in the hierarchy. The main conclusions: newer prediction indicators showed that (among 26 features) excessive pre-pregnancy weight/BMI and excessive GWG played a much more important role in macrosomia prediction than other maternal characteristics. These indicators more strongly highlighted the differences between predictors than the results of commonly used odds ratios.     

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。     

目标管理在医疗设备维修管理中的应用与效果分析

分析目标管理在医疗设备维修管理中的应用效果,选取2019年1月至2020年12月医疗设备维修数据,对比分析实施目标管理前后的设备整体平均维修时长、维修关单良好率、基础性设备(病床、监护仪及泵类)平均维修时长和工程师维修关单良好率,为医院维修管理精细化实践提供参考。     

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.