Utility of Differential White Cell Count and Cell Population Data for Ruling Out COVID-19 Infection in Patients With Community-Acquired Pneumonia

IntroductionThe main aim of this study was to assess the utility of differential white cell count and cell population data (CPD) for the detection of COVID-19 in patients admitted for community-acquired pneumonia (CAP) of different etiologies.MethodsThis was a multicenter, observational, prospective study of adults aged ≥18 years admitted to three teaching hospitals in Spain from November 2019 to November 2021 with a diagnosis of CAP. At baseline, a Sysmex XN-20 analyzer was used to obtain detailed information related to the activation status and functional activity of white cells.ResultsThe sample was split into derivation and validation cohorts of 1065 and 717 patients, respectively. In the derivation cohort, COVID-19 was confirmed in 791 patients and ruled out in 274 patients, with mean ages of 62.13 (14.37) and 65.42 (16.62) years, respectively (p < 0.001). There were significant differences in all CPD parameters except MO-Y. The multivariate prediction model showed that lower NE-X, NE-WY, LY-Z, LY-WY, MO-WX, MO-WY, and MO-Z values and neutrophil-to-lymphocyte ratio were related to COVID-19 etiology with an AUC of 0.819 (0.790, 0.846). No significant differences were found comparing this model to another including biomarkers (p = 0.18).ConclusionsAbnormalities in white blood cell morphology based on a few cell population data values as well as NLR were able to accurately identify COVID-19 etiology. Moreover, systemic inflammation biomarkers currently used were unable to improve the predictive ability. We conclude that new peripheral blood biomarkers can help determine the etiology of CAP fast and inexpensively.     

红外光谱数据融合对栽培滇重楼产地鉴别*

目的 多样的环境因素使得不同产地栽培滇重楼的化学成分也丰富多样，不同居群栽培滇重楼的甾体皂苷类成分具有很大的差异，多源数据融合分析能更全面的表征药材化学信息，建立一个高效而准确的产地鉴别模型，为其资源合理开发利用提供依据。方法 以来自云南和四川的8个产地（保山、楚雄、大理、红河、丽江、成都、文山、玉溪）共366份栽培滇重楼根茎为实验材料，采集其傅里叶变换近红外光谱（FT-NIR）和衰减全反射-傅里叶变换中红外光谱（ATR-FTMIR）数据。采用Kennard-Stone算法将不同产地的样品分为2/3的训练集和1/3的预测集，基于4种特征变量提取方法（CARS、VIP、SPA、SO-Covsel）结合2种数据融合策略（低级数据融合和中级数据融合），建立偏最小二乘产地判别分析模型。根据模型参数交叉验证均方根误差（RMSECV）和预测均方根误差（RMSEP）评估模型的稳定性，模型训练集和预测集准确率（ACC）评估模型分类性能。结果 近红外光谱和中红外光谱均能反应不同产地栽培滇重楼的化学成分差异，在中级数据融合中，基于VIP和SPA提取的特征变量建立的模型正确率均大于94%。相较于中级数据融合，低级数据融合模型得到了最为满意的结果，其预测集分类正确率达到100%。结论 根据近红外和中红外数据建立的低级数据融合PLS-DA模型，能够用于栽培滇重楼的产地鉴别分析。     

新型驱动基因融合阳性非小细胞肺癌脑转移的研究进展北大核心

肺癌是我国最常见且死亡率最高的恶性肿瘤之一,随着表皮生长因子受体(EGFR)、间变性淋巴瘤激酶(ALK)及活性氧1(ROS1)等驱动基因及其相应分子靶向药物的出现,晚期非小细胞肺癌的治疗和预后发生了革命性变化。此外,一些少见新型驱动基因融合,包括神经调节蛋白1(NRG1)、神经营养酪氨酸受体激酶(NTRK)和转染重排因子(RET),正逐渐发展成为全身性治疗选择。然而,这些罕见新型融合驱动基因在NSCLC脑转移中的作用机制,以及优化控制和预防脑转移的意义人们所知甚少。因此,本文就近几年罕见驱动基因融合在NSCLC脑转移中的研究进行综述。     

下一代诊断病理学

随着人类基因组测序、生物大数据信息分析、分子病理检测和人工智能辅助病理诊断等技术进步及其应用, 临床医学发展迈向精准诊疗时代。这一时代背景下, 传统诊断病理学迎来前所未有的历史机遇, 正在向"下一代诊断病理学(next-generation diagnostic pathology)"迈进。下一代诊断病理学以病理形态和临床信息为诊断基础, 以分子检测与生物信息分析、智慧制样与流程质控、智能诊断与远程会诊、病灶活体可视化与"无创"病理诊断等创新前沿交叉技术为主要特征, 以多组学和跨尺度整合诊断为病理报告内容, 实现对疾病的"最后诊断", 并预测疾病演进和结局、建议治疗方案和评估治疗反应, 形成新的疾病诊断"金标准"。未来, 需要激发病理学科创新活力, 加快下一代诊断病理学成熟和应用, 重塑病理学科理论和技术体系, 发挥诊断病理学在疾病"防、诊、治、养"等过程中的重要作用, 促进临床医学进一步发展, 服务健康中国战略。     

Effects of state reinsurance programs on health insurance exchange premiums and insurer participation

Objective

The aim of the study was to estimate the effect of the state-based reinsurance programs through the section 1332 State Innovation Waivers on health insurance marketplace premiums and insurer participation.

Data Source

2015 to 2022 Robert Wood Johnson Foundation Health Insurance Exchange Compare Datasets.

Study Design

An event study difference-in-differences (DD) model separately for each year of implementation and a synthetic control method (SCM) are used to estimate year-by-year effects following program implementation.

Data Collection/Extraction Methods

Not applicable.

Principal Findings

Reinsurance programs were associated with a decline in premiums in the first year of implementation by 10%–13%, 5%–19%, and 11%–17% for bronze, silver, and gold plans (p < 0.05). There is a trend of sustained declines especially for states that implemented their programs in 2019 and 2020. The SCM analyses suggest some effect heterogeneity across states but also premium declines across most states. There is no evidence that reinsurance programs affected insurer participation.

Conclusion

State-based reinsurance programs have the potential to improve the affordability of health insurance coverage. However, reinsurance programs do not appear to have had an effect on insurer participation, highlighting the need for policy makers to consider complementary strategies to encourage insurer participation.     

微创和传统开放后路腰椎椎间融合术治疗腰椎退行性疾病的远期预后

目的探讨微创后路腰椎椎间融合术(MIS-PLIF)和传统开放PLIF对腰椎退行性疾病(LDD)远期疗效及安全性的影响。方法 2011年1月-2014年12月收治LDD患者182例,其中96例采用传统开放PLIF治疗(PLIF组),86例采用MIS-PLIF治疗(MIS-PLIF组)。比较2组腰椎矢状位参数、多裂肌横截面积及萎缩率、融合率、疼痛视觉模拟量表(VAS)评分、日本骨科学会(JOA)评分、Oswestry功能障碍指数(ODI)及术后并发症发生情况,分析多裂肌萎缩率与顽固性腰背痛的相关性。结果 2组术后各随访时间点椎间隙高度恢复值和节段性前凸角恢复值差异均无统计学意义(P > 0.05)。2组术后1年腰椎前凸角恢复值差异无统计学意义(P > 0.05);但术后5年和末次随访时,MIS-PLIF组腰椎前凸角恢复值显著高于PLIF组,差异均有统计学意义(P < 0.05)。MIS-PLIF组术后各随访时间点多裂肌横截面积大于PLIF组,多裂肌萎缩率低于PLIF组,差异均有统计学意义(P < 0.05)。2组术后随访6个月融合率差异无统计学意义(P > 0.05)。2组术后各随访时间点下肢痛VAS评分差异无统计学意义(P > 0.05);MIS-PLIF组术后各随访时间点腰痛VAS评分、JOA评分及ODI均优于PLIF组,差异有统计学意义(P < 0.05)。MIS-PLIF组顽固性腰背痛发生率显著低于PLIF组,差异有统计学意义(P < 0.05)。合并顽固性腰背痛患者多裂肌萎缩率高于未合并顽固性腰背痛的患者,差异有统计学意义(P < 0.05)。结论术后多裂肌萎缩可能是导致顽固性腰背痛的重要原因,相较于传统开放PLIF,MIS-PLIF治疗LDD能够更有效地保持腰椎生理曲度,改善肢体活动功能,降低多裂肌萎缩程度,有助于避免顽固性腰背痛的发生。     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Mobile daily diaries to characterize stressors and acute health symptoms in an environmental justice neighborhood

Low-income communities and communities of color face multiple, cumulative environmental and social burdens. Methods development in environmental justice research has largely focused on spatial and quantitative approaches. Less attention has been paid to developing methodologies that help collect information on everyday stressors and quality of life experiences for residents in overburdened communities. Mixed methods approaches can be one way to structure study designs that help consider how residents experience environmental and socioeconomic impacts in a localized community context. In neighborhoods burdened by cumulative stressors, traditional cross-sectional epidemiological research designs can also be challenging, as well as limited or narrow in their application. However, repeat sampling of measures within a vulnerable population can approach a quasi-experimental design and help consider variations within residents in a single neighborhood as well as better parse relationships between exposures and outcomes. Through a community-academic partnership with university partners, local community partners, and a local promotores de salud (community health workers) network, we pilot tested a novel mobile daily diary approach in both English and Spanish in an urban, predominantly immigrant community in South Los Angeles as a potential method to collect information on daily stress, environmental quality, and health status/symptoms. We collected resident responses via a once per day 7-day SMS/text messaging survey. We sought to gather granular data on daily resident experiences of air pollution and environmental hazards. Residents reported acute health symptoms and stressors, with repeat measures demonstrating how residents might rank, categorize, or cope with stressors. We find that residents in environmental justice communities record variation in their daily diary responses and document changes in environmental quality, stressors, and odors. Refining this type of method could enable a more rigorous examination of co-occurrences of environmental quality and acute health symptoms. This approach supports the inclusion of residents in the research process and helps more systematically integrate open-ended environmental health relevant data in environmental justice efforts. Used with measured data such as air monitoring or health measures, mixed methods generated data can help support efforts that aim to alleviate sources of daily stress, alongside efforts to reduce overall pollution burdens. Mobile daily diaries can be one way to capture variable responses to environmental quality, acute health symptoms, and stressors.