Do social support and community engagement act as mechanisms in the association between neighbourhood income inequality and the mental health of mothers in Calgary,Canada? A mediation analysis

PurposeAccording to the social determinants of health framework, income inequality is a potential risk factor for adverse mental health. However, few studies have explored the mechanisms suspected to mediate this relationship. The current study addresses this gap through a mediation analysis to determine if social support and community engagement act as mediators linking neighbourhood income inequality to maternal anxiety and depressive symptoms within a cohort of new mothers living in the City of Calgary, Canada.MethodsData collected at three years postpartum from mothers belonging to the All Our Families (AOF) cohort were used in the current study. Maternal data were collected between 2012 and 2015 and linked to neighbourhood socioeconomic data from the 2006 Canadian Census. Income inequality was measured using Gini coefficients derived from 2006 after-tax census data. Generalized structural equation models were used to quantify the associations between income inequality and mental health symptoms, and to assess the potential direct and indirect mediating effects of maternal social support and community engagement.ResultsIncome inequality was not significantly associated with higher depressive symptoms (β = 0.32, 95%CI = −0.067, 0.70), anxiety symptoms (β = 0.11, 95%CI = −0.39, 0.60), or lower social support. Income inequality was not associated with community engagement. For the depression models, higher social support was significantly associated with lower depressive symptoms (β = −0.13, 95%CI = −0.15, −0.097), while community engagement was not significantly associated with depressive symptoms (β = 0.059, 95%CI = −0.15, 0.27). Similarly, for the anxiety models, lower anxiety symptoms were significantly associated with higher levels of social support (β = −0.17, 95%CI = −0.20, −0.13) but not with higher levels of community engagement (β = 0.14, 95%CI = −0.14, 0.41).ConclusionThe current study did not find clear evidence for social support or community engagement mediating the relationship between neighbourhood income inequality and maternal mental health. Future investigations should employ a broader longitudinal approach to capture changes in income inequality, potential mediators, and mental health symptomatology over time.     

EEG power spectral density under Propofol and its association with burst suppression,a marker of cerebral fragility

ObjectiveUnder General Anesthesia (GA), age and Burst Suppression (BS) are associated with cognitive postoperative complications, yet how these parameters are related to per-operative EEG and hypnotic doses is unclear. In this prospective study, we address this question comparing age and BS occurrences with a new score (BP_TIVA) based on Propofol doses, EEG and alpha-band power spectral densities, evaluated for SEF₉₅ = 8–13 Hz.Methods59 patients (55 [34–67] yr, 67% female) undergoing neuroradiology or orthopedic surgery were included. Total IntraVenous Anesthesia was used for Propofol and analgesics infusion. Cerebral activity was monitored from a frontal electrodes montage EEG.ResultsBP_TIVA was inversely correlated with age (Pearson r = −0.78, p < 0.001), and was significantly lower (p < 0.001) when BS occurred during the GA first minutes (induction). Additionally, the age-free BP_TIVA score was better associated with BS at induction than age (AUC = 0.94 versus 0.82, p < 0.05).ConclusionWe designed BP_TIVA score based on hypnotics and EEG. It was correlated with age yet was better associated to BS occurring during GA induction, the latter being a cerebral fragility sign.SignificanceThis advocate for an approach based on evaluating the cerebral physiological age (« brain age ») to predict postoperative cognitive evolution.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Aminotransferases During Treatment Predict Long-Term Survival in Patients With Autoimmune Hepatitis Type 1: A Landmark Analysis

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高效液相色谱串联质谱法测定氯沙坦钾中的遗传毒性杂质N-亚硝基-N-甲基-4-氨基丁酸

目的建立了一种高效液相色谱-三重四极杆串联质谱法,对氯沙坦钾原料药中遗传毒性杂质N-亚硝基-N-甲基-4-氨基丁酸(NMBA)进行测定。方法色谱柱为岛津Shim-pack XR-ODSⅡ色谱柱(2.0 mm×150 mm,2.2μm),流动相为0.1%甲酸水溶液(A)和甲醇(B),进行梯度洗脱,流速0.3 mL·min^-1,柱温为40℃,采用ESI离子化-三重四极杆质谱多反应监测(MRM)正离子模式检测,碰撞电压分别为-11,-13和-13 V,碰撞气氩气270 kPa,NMBA的离子对分别为m/z 147.15→117.10,147.15→87.10和147.15→44.10。结果该方法中NMBA在1~100 ng·mL^-1内线性关系良好,日内和日间的保留时间和峰面积的重复性良好(RSD均小于1.10%,n=6和n=18),低、中、高3个浓度的平均回收率在94.40%~98.04%之间。结论本方法简单方便,可快速有效的对氯沙坦钾原料药中NMBA进行限度检查并实现定量分析。     

关于生化自动分析仪噪音核查和反应限核查的几点讨论

生化自动分析仪在进行噪音核查和反应限核查之前，均已完成线性核查，已确定线性反应区，从吸光度-时间回归线计算出每分钟吸光度变化速度（△A／min）。在此基础上进行噪音核查（Noise Check）和反应限核查（Reaction Limit Check）。