dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

ObjectiveTo develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and MethodsThe GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.ResultsThe proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.ConclusionThe dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.     

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations.A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results.Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets.A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.     

The sexual brain,genes, and cognition: A machine‐predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9–10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC–AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p _fdr < .001, ηp2 = .011–.034; adjusted for covariates) and higher cognitive genome‐wide polygenic scores (GPSs) (p _fdr < .001, ηp2 < .005). Structural equation models revealed that the GPS‐intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006–.009; p = .002–.022; sex‐stratified analysis). The finding of the sex modulatory effect on the gene–brain–cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.     

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.     

Learning under social versus nonsocial uncertainty: A meta‐analytic approach

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.     

Decoupling location specificity from perceptual learning of orientation discrimination

Perceptual learning of orientation discrimination is reported to be precisely specific to the trained retinal location. This specificity is often taken as evidence for localizing the site of orientation learning to retinotopic cortical areas V1/V2. However, the extant physiological evidence for training improved orientation turning in V1/V2 neurons is controversial and weak. Here we demonstrate substantial transfer of orientation learning across retinal locations, either from the fovea to the periphery or amongst peripheral locations. Most importantly, we found that a brief pretest at a peripheral location before foveal training enabled complete transfer of learning, so that additional practice at that peripheral location resulted in no further improvement. These results indicate that location specificity in orientation learning depends on the particular training procedures, and is not necessarily a genuine property of orientation learning. We suggest that non-retinotopic high brain areas may be responsible for orientation learning, consistent with the extant neurophysiological data.     

Exploring the online learning experience of first-year speech–language pathology students in a Johannesburg-based university

BackgroundUnderstanding the learning experiences of first-year speech–language pathology (SLP) students during the coronavirus disease 2019 (COVID-19) pandemic is essential to ensure that academic staff are able to support and enhance the transition from secondary to tertiary education. An understanding of the student experience could lead to improved support strategies that could be beneficial for the blended learning environment that the University of the Witwatersrand will be entering from 2022.ObjectivesThis research explored the experiences of first-year SLP students in online learning during the COVID-19 pandemic.MethodAn exploratory mixed-method concurrent triangulation design was employed. Quantitative data were collected from likert scales. Qualitative data were collected from critical incident timelines. Themes were identified from both the Likert scales as well as the critical incident timelines using bottom-up thematic analysis.ResultsThe majority of participants reflected that their online learning through the pandemic in 2021 was successful. The themes that emerged from this year pertain to 2021 and the specific participants however, it provides an important insight that the students’ needs change during a year. As a lecturer, one needs to consider these evolving needs to ensure students have the support that they require to be successful in their learning.ConclusionThis research provided insights into the evolving nature of the support first-year SLP students require in the online learning space during the COVID-19 pandemic.     

地黄饮子对双转基因痴呆小鼠学习记忆及抗氧化能力的影响

目的研究地黄饮子对APP/PS1双转基因痴呆模型小鼠学习记忆、脑组织病理形态改变及抗氧化能力的影响。方法将40只APP/PS1双转基因痴呆小鼠分为模型组、西对组、中高组、中中组、中低组,每组8只;另以8只同背景同月龄的阴性小鼠为空白组。西对组给予盐酸多奈哌齐(0.66μg·g-1·d-1)灌胃,中高组、中中组、中低组给予地黄饮子相当于生药67.30 g·kg-1·d-1、33.65 g·kg-1·d-1、16.83 g·kg-1·d-1灌胃,模型组给予0.9%NaCl溶液灌胃。灌胃共4周后通过Morris水迷宫实验检测小鼠的学习和记忆能力;HE、尼氏染色检测小鼠脑组织的病理形态改变;采用比色法测定小鼠血清中超氧化物歧化酶(SOD)、丙二醛(MDA)、谷胱甘肽过氧化物酶(GSH-Px)水平。结果与模型组比较,中药各剂量组及西对组能缩短小鼠定位潜航实验的逃避潜伏期,差异有统计学意义(P0.05,P0.01);第2、4天西对组优于中低组(P0.05,P0.01),第5天西对组优于中中组、中低组(P0.05)。与模型组比较,中药各剂量组及西对组能提高小鼠穿越平台次数,差异有统计学意义(P0.05,P0.01)。与模型组比较,西对组、中药各剂量组SOD、GSH-Px含量增加,MDA含量下降,差异均有统计学意义(P0.05,P0.01)。与西对组比较,中低组、中中组SOD含量降低,差异有统计学意义(P0.05,P0.01)。结论地黄饮子可通过提高学习记忆能力、减轻脑组织神经元变性及脱失、提高抗氧化能力等途径起到防治老年性痴呆的作用。     

人工智能在乳腺超声中的应用与发展

人工智能是利用计算机模拟人类学习、思考及作出判断的技术和方法,在医学影像领域应用中为临床流程带来便利,也为疾病诊断、治疗和预后提供更多的信息。而超声技术与人工智能的结合为乳腺诊断带来革新,本文就人工智能在乳腺超声中的流程及方法、应用和发展作一综述。     

Working with communities, governments and academic institutions to make pregnancy safer

The major medical causes of maternal death and the effective interventions to prevent maternal death due to these causes are known. Yet, every year, an estimated 529,000 women die during and following pregnancy and childbirth. Most of these deaths occur in developing countries where other non-medical determinants of maternal health influence the accessibility to these interventions. Improvements in maternal health can be achieved through a health systems approach. Care should be provided as a continuum throughout the life cycle and across health facilities through the health system. Communities, professional organizations and academic institutions should work actively with the government to: provide a package of service, based on population health needs, that is close to home; ensure availability of essential medicines and commodities; address financial barriers to receiving care; strengthen the health workforce; and gather and use information to improve maternal health.