Learning optimal biomarker-guided treatment policy for chronic disorders.

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

Cavity-enhanced optical bistability of Rydberg atoms.

Optical bistability (OB) of Rydberg atoms provides a new, to the best of our knowledge, platform for studying nonequilibrium physics and a potential resource for precision metrology. To date, the observation of Rydberg OB has been limited in free space. Here, we explore cavity-enhanced Rydberg OB with a thermal cesium vapor cell. The signal of Rydberg OB in a cavity is enhanced by more than one order of magnitude compared with that in free space. The slope of the phase transition signal at the critical point is enhanced more than 10 times that without the cavity, implying an enhancement of two orders of magnitude in the sensitivity for Rydberg-based sensing and metrology.

Realization of Strong Coupling between Deterministic Single-Atom Arrays and a High-Finesse Miniature Optical Cavity.

We experimentally demonstrate strong coupling between a one-dimensional (1D) single-atom array and a high-finesse miniature cavity. The atom array is obtained by loading single atoms into a 1D optical tweezer array with dimensions of 1×11. Therefore, a deterministic number of atoms is obtained, and the atom number is determined by imaging the atom array on a CCD camera in real time. By precisely controlling the position and spacing of the atom array in the high finesse Fabry-Perot cavity, all the atoms in the array are strongly coupled to the cavity simultaneously. The vacuum Rabi splitting spectra are discriminated for deterministic atom numbers from 1 to 8, and the sqrt[N] dependence of the collective enhancement of the coupling strength on atom number N is validated at the single-atom level.

A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

Multilayer Exponential Family Factor models for integrative analysis and learning disease progression.

Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.

Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased R t $$ {R}_t $$ associated with reopening bars.

Resilience, coping style, and COVID-19 stress: effects on the quality of life in frontline health care workers.

The aims of the study were to assess the contribution of resilience, coping style, and COVID-19 stress on the quality of life (QOL) in frontline health care workers (HCWs). The study was a cross-sectional surveyperformed among 309 HCWs in a tertiaryhospital during the outbreak of COVID-19 in China. Data were collected through an anonymous, self-rated questionnaire, including demographic data, a 10-item COVID-19 stress questionnaire, Generic QOL Inventory-74, Connor-Davidson Resilience Scale, and the Simplified Coping Style Questionnaire. Hierarchical regression was used to analyse the relationship between the study variables and the QOL. Among the 309 participants, resilience and active coping were positively correlated with the QOL (P<0.001), whereas, working in confirmed case wards, COVID-19 stress, and passive coping were negatively correlated with the QOL (P<0.001). Resilience and the active coping were negatively correlated with COVID-19 stress (P<0.001). Resilience, coping style,and COVID-19 stressaccounted for 32%, 13%, and 8% of the variance in predicting the Global QOL, respectively. In conclusion, working in confirmed COVID-19 case wards and COVID-19 stress impaired the QOL in HCWs. Psychological intervention to improve the resilience and coping style, and reduce COVID-19 stress are important in improving the QOL and mental health of HCWs.

Evaluating Effectiveness of Public Health Intervention Strategies for Mitigating COVID-19 Pandemic.

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-white population are at greater risk of increased $R_t$ associated with reopening bars.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model.

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.

Anxiety and depression in frontline health care workers during the outbreak of Covid-19.

BACKGROUND: The pandemic of coronavirus disease (Covid-19) seriously impacts the health and well-being of all of us. AIMS: We aim to assess the psychological impact of Covid-19 on frontline health care workers (HCWs), including anxiety, depression and stress of threat of the disease. METHOD: The study was a cross-sectional survey among the frontline HCWs in a hospital at Jinan, China. Data were collected through an anonymous, self-rated questionnaire, including basic demographic data, a 10-item Covid-19 stress questionnaire, the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS). The risk and rate of anxiety, depression and stress of Covid-19 were estimated. RESULTS: Among the 309 participants, there were 88 (28.5%) with anxiety and 172 (56.0%) with depression. Multivariate logistic regression analyses showed that age ⩽ 30 years, age > 30 to 45 years, working in confirmed case isolation wards, and worrying about disinfection measures being not sufficient were independently associated with anxiety with an odds ratio (95% confidence interval, CI) of 4.4 (1.6-12.2), 3.1 (1.1-8.8), 2.3 (1.4-4.0) and 2.5 (1.5-4.3), respectively; age ⩽ 30 years, age > 30 to 45 years, nurse and worrying about disinfection measure being not sufficient were independently associated with depression with an odds ratio (95% CI) of 3.8 (1.8-7.8), 2.7 (1.3-5.7), 2.5 (1.1-5.6) and 2.1 (1.3-3.5), respectively. CONCLUSIONS: A high prevalence of anxiety and depression was found among frontline HCWs during the COVID-19 outbreak. More psychological care should be given to young staffs and nurses. Measures to prevent professional exposure is important for HCWs' physical and mental health.

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in Rt was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (R t ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only data in the early phase (two to three weeks after the outbreak). A fast rate of decline in R t was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The lockdown in Italy did not further accelerate the speed at which the infection rate decreases. The effective reproduction number has staggered around R t = 1.0 for more than 2 weeks before decreasing to below 1.0, and the epidemic in Italy is currently under control. In the US, R t significantly decreased during a 2-week period after the declaration of national emergency, but afterwards the rate of decrease is substantially slower. If the trend continues after May 1, the first wave of COVID-19 may be controlled by July 26 (CI: July 9 to August 27). However, a loss of temporal effect on infection rate (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (November 19 with less than 100 daily cases) and a total of more than 2 million cases.

An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images.

In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two-stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal-dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR-T1\MR-T2 images, and the comparison metrics SF, MI, Q A B / F , Q W , and VIFF are calculated for assessment. Compared with seven state-of-the-art methods, the proposed method shows a comprehensive advantage in preserving the salient intensity features, as well as texture structure information, not only in visual effects but also in objective assessments.

A conjugate of methotrexate and an analog of luteinizing hormone releasing hormone shows increased efficacy against prostate cancer.

LHRH receptor, is over-expressed in a variety of human tumors and, is a potential binding site for targeted metastatic prostate cancer therapy. The objectives of our study were to synthesize a bioconjugate of the LHRH analog [DLys6]-LHRH and the anti-tumor agent methotrexate and test the hypothesis that [DLys6]-LHRH-MTX targets and inhibits prostate cancer cell growth in vitro and in vivo. The results of in vitro studies, showed that both [DLys6]-LHRH-MTX and MTX displayed superior cytotoxicity against prostate cancer cells in a concentration-dependent manners, with IC50 concentrations for PC-3 cells of, 1.02 ± 0.18 µmol/L and 6.34 ± 1.01 µmol/L; for DU-145 cells, 1.53 ± 0.27 µmol/L and 8.03 ± 1.29 µmol/L; and for LNCaP cells, 1.93 ± 0.19 µmol/L and 9.68 ± 1.24 µmol/L, respectively. The IC50 values of [DLys6]-LHRH-MTX and MTX were 110.77 ± 15.31 µmol/L and 42.33 ± 7.25 µmol/L, respectively. Finally, [DLys6]-LHRH-MTX significantly improved the anti-tumor activity of MTX in nude mice bearing PC-3 tumor xenografts. The inhibition ratios of tumor volume and tumor weight in the [DLys6]-LHRH-MTX treated group were significantly higher than those in the MTX-treated group. Tumor volume doubling time was also significantly extended from 6.13 days in control animals to 9.67 days in mice treated with [DLys6]-LHRH-MTX. In conclusion, [DLys6]-LHRH -MTX may be useful in treating prostate cancer.

[Analysis of flavonoids and components of stems & leaves of Ranunculus ternatus Thunb. using ultra performance liquid chromatography-quadrupole tandem time of flight mass spectrometry].

In order to explore the applications of liquid chromatography-mass spectrometry (LC-MS) technology in the rapid identification of components of Chinese herbal medicines and natural products, reference flavones were used as precursors, and the stems and leaves of medicinal plant Ranunculus ternatus Thunb. were used as the objects. Ultra performance liquid chromatography with a diode array detector-electrospray ionization quadrupole tandem time of flight mass spectrometry (UPLC/DAD-ESI/Q-TOF MS) was also used to analyse the characteristics of flavonoid homologues and flavonoid isomers. The results showed that ultraviolet (UV) absorption and MS/MS spectra of C-glycosyl were distinct from O-glycosyl. There was also a correlation between glycosidation positions and the retention times, MS/MS fragments and their relative abundances. When applied it to analyse the alcohol extract of stems and leaves of Ranunculus ternatus Thunb. and their acid hydrolysis solution, 22 flavonol glycosides and 3 flavonoid aglycones were identified. The method is simple, convenient and exercisable.