Lack of Protection Against Ebola Virus from Chloroquine in Mice and Hamsters

The antimalarial drug chloroquine has been suggested as a treatment for Ebola virus infection. Chloroquine inhibited virus replication in vitro, but only at cytotoxic concentrations. In mouse and hamster models, treatment did not improve survival. Chloroquine is not a promising treatment for Ebola. Efforts should be directed toward other drug classes.     

运用Mobley模型分析护理人员流失原因及对策

目的：运用Mobley模型分析护理人员流失的影响因素,为护理管理者有针对性地采取措施提供依据。方法采用问卷调查法对2011—2013年某三级甲等综合医院流失的护理人员基本情况和离职原因进行调查,并运用Mobley模型分析离职影响因素。结果影响因素中,非工作价值观及偶然因素所占比例最大（56．39％）,其次为工作满意度（21．81％）和组织内、外工作角色及收益预期（21．80％）。结论建立良好的工作环境,实行专业化的岗位管理,倡导“磁性”的管理理念,是提高护士工作满意度,稳定护理队伍的关键。     

全程优质护理对口腔颌面外科手术患者的心理影响

目的：研究探讨全程优质护模式对口腔颌面外科手术患者负性心理情绪的影响。方法：选取实施口腔颌面部外科手术的患者100例随机分为两组,每组50例,对照组患者给予传统护理,观察组患者则实施全程优质护理。比较其治疗前后的SAS评分和SDS评分。结果：两组患者治疗前的SAS评分和SDS评分之间均无显著差异（P>0.05）,治疗后均显著改善,与治疗前的比较有统计学差异（P<0.05）。观察组患者治疗后的焦虑评分和抑郁评分均显著优于对照组患者,有统计学差异（P<0.05）。结论：对实施口腔颌面外科手术的患者实施全程优质护理,能有效地改善患者的负性心理情绪,促进患者术后的恢复,值得临床推广应用。     

医院病理样本库管理系统的设计

目的:建立病理样本数据库,以规范病理样本管理流程,并为病理样本存储和研究提供工作平台。方法:病理样本库管理系统采用了J2EE、JSP及组件对象模型等技术,建立了病理样本库工作平台,实现了病理资源网络化、文档化及智能化。结果:该系统建立了病理样本管理系统架构和工作流程,为病理样本的收集、存储及分析提供了平台。结论:系统设计可提高样本检定结果的真实性和可信度,从而提高样本分析的准确性,为相关科室的信息化建设提供了良好的借鉴。     

激光治疗闭角型青光眼日间病房管理模式的探讨

日间病房是一种以病人为中心,介于门诊与住院之间的诊疗模式,是对传统医疗模式的补充,具有提高床位周转率、缩短住院天数、降低费用、缓解医患矛盾等优点,其发展具有广阔前景。首都医科大学宣武医院眼科为适应医疗服务发展需要,开设了激光治疗青光眼日间病房,为医院取得了良好的社会效益和经济效益。     

Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure

Introduction

Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen).

Materials and methods

We assessed the accuracy of the models’ predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1 h. The time-course of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated.

Results

With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52–4.19 mg/L with the Bos model, 1.42–4.04 mg/L with the Mumtaz model, and 1.81–4.31 mg/L with the Jongeneelen model compared to 0.27–5.44 mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4–2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels.

Conclusions

The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment.     

职业紧张与非致死性职业伤害关系的研究

目的评价职业紧张和非致死性职业伤害的关系。方法采用1:1配比的病例-对照调查设计,以2013年10—12月在中山市6家工伤定点医院收治的151名职业意外伤害工人和151名经年龄、性别、工种匹配且在过去一年无意外伤害的同事为调查对象,运用工作内容问卷(Job Content Questionnaire,JCQ)测量职业紧张。结果两组工人的职业紧张包括自主性维度与工作要求维度与对照组相比差异均有统计学意义(均P0.01)。社会支持方面,上级支持维度得分两组差异有统计学意义(P0.05)。使用多因素COX回归拟合条件logistic回归分析结果显示,工作需求高(OR=1.27,95%CI=1.15~1.42)是职业性意外伤害的危险因素,而工作自主性高(OR=0.82,95%CI=0.74~0.91)及上级支持较高(OR=0.84,95%CI=0.69~1.00)是职业性意外伤害的保护因素。结论职业紧张对非致死性职业伤害的发生存在影响。     

不可切除胆道癌致梗阻性黄疸患者行介入术后的生存预测模型建立

目的探讨因不可切除胆道癌致阻黄患者行介入治疗后预后生存的影响因素,并构建列线图个体化预测患者总生存期（OS）。 方法回顾性分析我院2015年6月至2021年7月收治的261例胆道癌行经皮介入降黄患者的资料。总体划分为训练组（n = 188）及验证组（n = 73）。单因素和多因素Cox回归分析筛选患者OS的独立影响因素,并以此构建列线图,个体化预测不可切除胆道癌伴有阻黄患者介入治疗术后生存情况。绘制AUC曲线及一致性曲线评估模型预测性能。通过K-M曲线评估分类预测因子的危险分层能力。 结果在261例患者中,训练组中位OS为244 d（IQR：213,275）。验证组中位OS为236 d（IQR：186,285）。单因素及多因素回归分析显示,总胆红素、总胆固醇、血红蛋白、血清钠、糖类抗原199水平和肿瘤亚型为预测不可切除胆道癌伴阻黄患者3、6个月和1年的OS的独立相关因素,并建立诺模图。ROC曲线下面积显示,该模型在训练组（0.817、0.825和0.796）和验证组（0.921、0.880和0.904）对3、6个月和1年的OS预测具有良好的分辨能力,可以较准确地预测患者的总体生存情况。 结论本模型能够较好预测不可切除胆道癌伴阻黄患者3、6个月、1年的生存机会,为临床治疗策略选择提供一定的帮助。     

Understanding the role of foot biomechanics on regional foot orthosis deformation in flatfoot individuals during walking

BackgroundFoot orthoses (FOs) are one of the most common interventions to restore normal foot mechanics in flatfoot individuals. New technologies have made it possible to deliver customized FOs with complex designs for potentially better functionalities. However, translating the individuals’ biomechanical needs into the design of customized FOs is not yet fully understood.Research questionOur objective was to identify whether the deformation of customized FOs is related to foot kinematics and plantar pressure during walking.MethodsThe kinematics of multi-segment foot and FOs contour were recorded together with plantar pressure in 17 flatfoot individuals while walking with customized FOs. The deformation of FOs surface was predicted from its contour kinematics using an artificial neural network. Plantar pressure map and deformation were divided into five anatomically based regions defined by the corresponding foot segments. Forward stepwise linear mixed models were built for each of the four gait phases to determine the feet-FOs interaction.ResultsIt was observed that some associations existed between foot kinematics and pressure with regional FOs deformation. From heel-strike to foot-flat, longitudinal arch angle was associated with FOs deformation in forefoot. From foot-flat to midstance, rearfoot eversion accounted for variation in the deformation of medial FOs regions, and forefoot abduction for the lateral regions. From midstance to heel-off, rearfoot eversion, longitudinal arch angle, and plantar pressure played significant role in deformation. Finally, from heel-off to toe-off, forefoot adduction affected the deformation of forefoot and midfoot.SignificanceThis study provides guidelines for designing customized FOs. Flatfoot individuals with excessive rearfoot eversion or very flexible medial arches require more support on medial FOs regions, while the ones with excessive forefoot abduction need the support on lateral regions. However, a compromise should be made between the level of support and the level of increase in plantar pressure to avoid stress on foot structures.     

Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example

ObjectiveAddress model drift in a machine learning (ML) model for predicting diagnostic imaging follow-up using data augmentation with more recent data versus retraining new predictive models.MethodsThis institutional review board–approved retrospective study was conducted January 1, 2016, to December 31, 2020, at a large academic institution. A previously trained ML model was trained on 1,000 radiology reports from 2016 (old data). An additional 1,385 randomly selected reports from 2019 to 2020 (new data) were annotated for follow-up recommendations and randomly divided into two sets: training (n = 900) and testing (n = 485). Support vector machine and random forest (RF) algorithms were constructed and trained using 900 new data reports plus old data (augmented data, new models) and using only new data (new data, new models). The 2016 baseline model was used as comparator as is and trained with augmented data. Recall was compared with baseline using McNemar’s test.ResultsFollow-up recommendations were contained in 11.3% of reports (157 or 1,385). The baseline model retrained with new data had precision = 0.83 and recall = 0.54; none significantly different from baseline. A new RF model trained with augmented data had significantly better recall versus the baseline model (0.80 versus 0.66, P = .04) and comparable precision (0.90 versus 0.86).DiscussionML methods for monitoring follow-up recommendations in radiology reports suffer model drift over time. A newly developed RF model achieved better recall with comparable precision versus simply retraining a previously trained original model with augmented data. Thus, regularly assessing and updating these models is necessary using more recent historical data.