Discovery of a Highly Potent and Selective BRD9 PROTAC Degrader Based on E3 Binder Investigation for the Treatment of Hematological Tumors.

BRD9 is a pivotal epigenetic factor involved in cancers and inflammatory diseases. Still, the limited selectivity and poor phenotypic activity of targeted agents make it an atypically undruggable target. PROTAC offers an alternative strategy for overcoming the issue. In this study, we explored diverse E3 ligase ligands for the contribution of BRD9 PROTAC degradation. Through molecular docking, binding affinity analysis, and structure-activity relationship study, we identified a highly potent PROTAC E5, with excellent BRD9 degradation (DC50 = 16 pM) and antiproliferation in MV4-11 cells (IC50 = 0.27 nM) and OCI-LY10 cells (IC50 = 1.04 nM). E5 can selectively degrade BRD9 and induce cell cycle arrest and apoptosis. Moreover, the therapeutic efficacy of E5 was confirmed in xenograft tumor models, accompanied by further RNA-seq analysis. Therefore, these results may pave the way and provide the reference for the discovery and investigation of highly effective PROTAC degraders.

Dual-stream EfficientNet with adversarial sample augmentation for COVID-19 computer aided diagnosis.

Though a series of computer aided measures have been taken for the rapid and definite diagnosis of 2019 coronavirus disease (COVID-19), they generally fail to achieve high enough accuracy, including the recently popular deep learning-based methods. The main reasons are that: (a) they generally focus on improving the model structures while ignoring important information contained in the medical image itself; (b) the existing small-scale datasets have difficulty in meeting the training requirements of deep learning. In this paper, a dual-stream network based on the EfficientNet is proposed for the COVID-19 diagnosis based on CT scans. The dual-stream network takes into account the important information in both spatial and frequency domains of CT scans. Besides, Adversarial Propagation (AdvProp) technology is used to address the insufficient training data usually faced by the deep learning-based computer aided diagnosis and also the overfitting issue. Feature Pyramid Network (FPN) is utilized to fuse the dual-stream features. Experimental results on the public dataset COVIDx CT-2A demonstrate that the proposed method outperforms the existing 12 deep learning-based methods for COVID-19 diagnosis, achieving an accuracy of 0.9870 for multi-class classification, and 0.9958 for binary classification. The source code is available at https://github.com/imagecbj/covid-efficientnet.

Dual inhibition of CHK1/FLT3 enhances cytotoxicity and overcomes adaptive and acquired resistance in FLT3-ITD acute myeloid leukemia.

FLT3 inhibitors (FLT3i) are widely used for the treatment of acute myeloid leukemia (AML), but adaptive and acquired resistance remains a primary challenge. Inhibitors simultaneously blocking adaptive and acquired resistance are highly demanded. Here, we observed the potential of CHK1 inhibitors to synergistically improve the therapeutic effect of FLT3i in FLT3-mutated AML cells. Notably, the combination overcame adaptive resistance. The simultaneous targeting of FLT3 and CHK1 kinases may overcome acquired and adaptive resistance. A dual FLT3/CHK1 inhibitor 30 with a good oral PK profile was identified. Mechanistic studies indicated that 30 inhibited FLT3 and CHK1, downregulated the c-Myc pathway and further activated the p53 pathway. Functional studies showed that 30 was more selective against cells with various FLT3 mutants, overcame adaptive resistance in vitro, and effectively inhibited resistant FLT3-ITD AML in vivo. Moreover, 30 showed favorable druggability without significant blood toxicity or myelosuppression and exhibited a good oral PK profile with a T1/2 over 12 h in beagles. These findings support the targeting of FLT3 and CHK1 as a novel strategy for overcoming adaptive and acquired resistance to FLT3i therapy in AML and suggest 30 as a potential clinical candidate.

Real-time COVID-19 detection over chest x-ray images in edge computing.

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.

Life-Events Mediate the Prediction of Parental Alienation on Depression in Rural Left-Behind Children: A Longitudinal Study.

Objectives: Long-time separation with parents during early life, such as left-behind children (LBC, one or both of whose parents are leaving for work for at least a period of 6 months), may contribute to high alienation toward parents and endanger their mental health (e.g., depression). However, the dynamic status of depression and potential prediction of alienation on depression in LBC remained largely unknown. This study aimed to examine the dynamic status of depression, prediction of alienation toward parents on later depression in rural LBC, and a potential mediation of life-events. Methods: A total of 877 LBC in rural areas of China were recruited and surveyed at five time-points (baseline, T0: 1-month, T1: 3-months, T2: 6-months, T3: 12-months, T4) with the Inventory of Alienation Toward Parents, Childhood Depression Inventory, and Adolescent Self-Rating Life-Events Checklist. The Hierarchical Linear Model (HLM) and Hayes's PROCESS macro model were conducted to estimate the developmental trend and hierarchical predictors of depression. Results: The left-behind children aged 9-years old experienced higher depression than the children with other ages. At baseline, the children in the family atmosphere of frequent quarrels and compulsive parenting style reported a higher level of alienation toward parents, life-events, and depression. Alienation toward parents, life-events, and depression were positively and moderately correlated with each other (r = 0.14 ~ 0.64). The HLM model depicted a linear decline in depression, alienation, and life-events with an average rate of 0.23, 0.24, and 0.86, respectively, during the five time-points. Also, T0 alienation toward parents and T0 life-events positively predicted the developmental trajectory of depression over time, and T0 life-events positively predicted the descendant rate of depression. Notably, life-events mediated the prediction of baseline alienation toward parents on T4 depression in LBC. Conclusion: This study is among the first to reveal that alienation toward parents predicts the developmental trajectory of later depression in LBC. The findings that life-events mediate the prediction of alienation on later depression further suggest the importance of family and social factors in the occurrence of depression in LBC. The findings warrant the necessity to consider the family and social factors when evaluating and reducing risks for mental health problems in LBC, i.e., relationship with parents (especially alienation toward parents) and life-events need further attention.

Problematic utilization of online social networking site in Chinese college students: prediction of personality and dynamic mediators.

Utilization of online social networking sites (SNSs) is often problematic in young people. However, studies seldom seek to understand personal differences and deep-seated reasons in its problematic utilization. This study aims to explore the longstanding and recent psychosocial predictors of problematic utilization of WeChat friend center (PUWF) longitudinally. A total of 433 college students (17-25 years old, male/female ratio: 389/44) were investigated over 2 successive years (T1: first year; T2: second year) using the Sixteen Personality Factor Questionnaire, Adolescent Self-Rating Life Events Checklist, Social Support Scale, Patient Health Questionnaire, Connor-Davidson Resilience Scale, and the problematic utilization scale of the WeChat friend center which was developed in this study. Correlation, regression, and structural equation analyses were conducted. A problematic utilization scale of the WeChat friend center was developed with Cronbach's alpha of .836. 21.02% of students reported WeChat PUWF. Males utilized the WeChat friend center less than females, and females were at higher risk of PUWF, which was correlated with worse mental health. In the longitudinal prediction, regression and modeling analyses showed that apprehension of personality predicted PUWF consistently and directly, and this was partially mediated by T1 depression and T2 negative life events. Resultys suggest that females are at higher risk for PUWF. Apprehension personality has a direct and indirect effect on PUWF through recent depression and life events. The findings help to recognize individuals at risk for PUWF as well as to better prevent it, and provide suggestions as to the functional design of SNSs according to different need of users. Core tips: Utilization of SNSs is often problematic in young people. However, personal differences and deep-seated reasons in its problematic utilization has been poorly revealed. Through a longitudinal investigation, this study confirms that females are at higher risk for PUWF. Apprehension personality has a direct and indirect effect on PUWF through recent depression and life events. The findings help to recognize individuals at risk for PUWF and give theoretical evidence to the functional design of SNSs for diferent users. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-03150-7.

Impact of COVID-19 on psychological wellbeing / Impacto del COVID-19 sobre el bienestar psicológico

This cross-sectional study aims to record post-traumatic stress (PTS) and post-traumatic growth (PTG) of the general population of China during the first wave of COVID-19 spread. Method: An online survey was distributed in China during February and March 2020 to record the general population's PTS (using the Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) and PTG (using the Post-traumatic Growth Inventory, PTGI) due to COVID-19. Confirmatory Factor Analyses (CFAs) and a Two-Part Model (TPM) of regression analysis were conducted. Results: In total, 29,118 Chinese participants completed the survey (54.20% were in their 20s, 68% were males, and 60.30% had a university education). CFA results illustrated that bifactor models described the Chinese psychometric traits of PTS and PTG over the default models. Results of TPM suggested that female, low-educated, and middle-aged individuals were more vulnerable to PTS. Remarkably, mutual and positive correlations between the PTS and the PTG, though small in statistics, were observed through regression analyses. Conclusions: The current results presented new best-fit structural models, potential predictors, and valuable baseline information on the PTS and the PTG of the Chinese population in the context of COVID-19. (AU)

Este estudio transversal se realizó para registrar el estrés postraumático (EPT) y el crecimiento de estrés postraumático (CPT) de la población general de China durante la primera ola de la extensión del COVID-19. Método: Se realizó una encuesta en línea en China durante febrero y marzo del año 2020 para registrar EPT de la población (utilizando el Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) y CPT (utilizando el Post-traumatic Growth Inventory, PTGI). Se llevaron a cabo Análisis Factorial Confirmatorio (AFC) y Modelo de Dos Partes (MDP) de análisis de regresión. Resultados: En total, 29.118 chinos completaron la encuesta (54,2% de ellos tenían 20~29 años, 68,0% eran hombres, y 60,3% tenían una Educación Universitaria). Los resultados de AFC ilustraron que los modelos de bifactoriales eran mejores para descubrir los rasgos psicométricos de EPT y CPT de los participantes chinos que los modelos predeterminados. Los resultados de MDP sugirieron que las mujeres, las personas con bajo nivel educativo y de mediana edad eran más vulnerables a EPT. Se observaron correlaciones mutuas y positivas entre EPT y CPT, aunque pequeñas. Conclusiones: Los resultados actuales presentaron nuevos modelos estructurales de mejor ajuste, predictores potenciales e información de referencia valiosa de EPT y CPT de la población China en el contexto de COVID-19. (AU)

Impact of COVID-19 on psychological wellbeing.

This cross-sectional study aims to record post-traumatic stress (PTS) and post-traumatic growth (PTG) of the general population of China during the first wave of COVID-19 spread. Method: An online survey was distributed in China during February and March 2020 to record the general population's PTS (using the Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) and PTG (using the Post-traumatic Growth Inventory, PTGI) due to COVID-19. Confirmatory Factor Analyses (CFAs) and a Two-Part Model (TPM) of regression analysis were conducted. Results: In total, 29,118 Chinese participants completed the survey (54.20% were in their 20s, 68% were males, and 60.30% had a university education). CFA results illustrated that bifactor models described the Chinese psychometric traits of PTS and PTG over the default models. Results of TPM suggested that female, low-educated, and middle-aged individuals were more vulnerable to PTS. Remarkably, mutual and positive correlations between the PTS and the PTG, though small in statistics, were observed through regression analyses. Conclusions: The current results presented new best-fit structural models, potential predictors, and valuable baseline information on the PTS and the PTG of the Chinese population in the context of COVID-19.

Este estudio transversal se realizó para registrar el estrés postraumático (EPT) y el crecimiento de estrés postraumático (CPT) de la población general de China durante la primera ola de la extensión del COVID-19. Método: Se realizó una encuesta en línea en China durante febrero y marzo del año 2020 para registrar EPT de la población (utilizando el Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) y CPT (utilizando el Post-traumatic Growth Inventory, PTGI). Se llevaron a cabo Análisis Factorial Confirmatorio (AFC) y Modelo de Dos Partes (MDP) de análisis de regresión. Resultados: En total, 29.118 chinos completaron la encuesta (54,2% de ellos tenían 20~29 años, 68,0% eran hombres, y 60,3% tenían una Educación Universitaria). Los resultados de AFC ilustraron que los modelos de bifactoriales eran mejores para descubrir los rasgos psicométricos de EPT y CPT de los participantes chinos que los modelos predeterminados. Los resultados de MDP sugirieron que las mujeres, las personas con bajo nivel educativo y de mediana edad eran más vulnerables a EPT. Se observaron correlaciones mutuas y positivas entre EPT y CPT, aunque pequeñas. Conclusiones: Los resultados actuales presentaron nuevos modelos estructurales de mejor ajuste, predictores potenciales e información de referencia valiosa de EPT y CPT de la población China en el contexto de COVID-19.

Fear in the Chinese Population: Influential Patterns in the Early Stage of the COVID-19 Pandemic.

Major global public health emergencies challenge public mental health. Negative emotions, and especially fear, may endanger social stability. To better cope with epidemics and pandemics, early emotional guidance should be provided based on an understanding of the status of public emotions in the given circumstances. From January 27 to February 11, 2020 (during which the cases of COVID-19 were increasing), a national online survey of the Chinese public was conducted. A total of 132,482 respondents completed a bespoke questionnaire, the Emotion Regulation Questionnaire, and the Berkeley Expressivity Questionnaire (BEQ). Results showed that at the early stage of the COVID-19 epidemic, 53.0% of the Chinese population reported varying degrees of fear, mostly mild. As seen from regression analysis, for individuals who were unmarried and with a relatively higher educational level, living in city or area with fewer confirmed cases, cognitive reappraisal, positive expressivity and negative inhibition were the protective factors of fear. For participants being of older age, female, a patient or medical staff member, risk perception, negative expressivity, positive impulse strength and negative impulse strength were the risk factors for fear. The levels of fear and avoidant behavior tendencies were risk factors for disturbed physical function. Structural equation modeling suggested that fear emotion had a mediation between risk perception and escape behavior and physical function disturbance. The findings help to reveal the public emotional status at the early stage of the pandemic based on a large Chinese sample, allowing targeting of the groups that most need emotional guidance under crisis. Findings also provide evidence of the need for psychological assistance in future major public health emergencies.

Negative Emotions in Chinese Frontline Medical Staff During the Early Stage of the COVID-19 Epidemic: Status, Trend, and Influential Pathways Based on a National Investigation.

Objective: The outbreak of coronavirus disease 2019 (COVID-19), declared as a major public health emergency, has had profound effects on public mental health especially emotional status. Due to professional requirements, medical staff are at a higher risk of infection, which might induce stronger negative emotions. This study aims to reveal the emotional status of Chinese frontline medical staff in the early epidemic period to better maintain their mental health, and provide adequate psychological support for them. Methods: A national online survey was carried out in China at the early stage of the COVID-19 epidemic. In total, 3025 Chinese frontline medical staff took part in this investigation which utilized a general information questionnaire, the Emotion Regulation Questionnaire (ERQ), and the Berkeley Expressivity Questionnaire (BEQ). Results: At the early stage of COVID-19, anxiety was the most common negative emotion of Chinese medical staff, followed by sadness, fear, and anger, mainly at a mild degree, which declined gradually over time. Nurses had the highest level of negative emotions compared with doctors and other healthcare workers. Women experienced more fear than men, younger and unmarried medical staff had more anxiety and fear compared with elders and married ones. Risk perception and emotional expressivity increased negative emotions, cognitive reappraisal reduced negative emotions, while negative emotions led to more avoidant behavior and more physical health disturbances, in which negative emotions mediated the effect of risk perception on avoidant behavior tendency in the model test. Conclusion: Chinese frontline medical staff experienced a mild level of negative emotions at the early stage of COVID-19, which decreased gradually over time. The findings suggest that during the epidemic, nurses' mental health should be extensively attended to, as well as women, younger, and unmarried medical staff. To better ensure their mental health, reducing risk perception and improving cognitive reappraisal might be important, which are potentially valuable to form targeted psychological interventions and emotional guidance under crisis in the future.

Sleep quality and its impacts on quality of life among military personnel in remote frontier areas and extreme cold environments.

BACKGROUND: Poor sleep quality negatively affects the readiness of military operations and is also associated with the development of mental health disorders and decreased quality of life. The purpose of this study was to investigate the sleep quality of military personnel from remote boundaries of China and its relationship with coping strategies, anxiety, and health-related quality of life (HRQoL). METHODS: A cross-sectional survey was performed among military officers and soldiers from a frontier defence department and an extreme cold environment. The participants were surveyed using the Pittsburgh Sleep Quality Index (PSQI), Trait Coping Style Questionnaire (TCSQ), Self-rating Anxiety Scale (SAS), and Short Form Health Survey (SF-36). RESULTS: A total of 489 military officers and soldiers were included. The participants had a mean age of 22.29 years. The median overall PSQI score was 7.0 (IQR, 4.0 ~ 9.0), with 40.9% (200/489) of the subjects reporting poor sleep quality. The difficulties with sleep were mainly related to daytime dysfunction due to disrupted sleep, sleep latency, and subjective sleep quality. The median score of the SF-36 physical component was 83.5 (IQR, 73.0 ~ 90.5), and the median score of the mental component was 74.1 (IQR, 60.4 ~ 85.1). Significant correlations were found between the PSQI and SF-36 (r = - 0.435, P <  0.01). Anxiety symptoms, marital status, educational background, and global PSQI score were demonstrated as predictors of a low SF-36 physical component by multiple regression analysis (F = 17.06, P <  0.001, R2 = 0.117). CONCLUSIONS: Sleep difficulty is a prevalent and underestimated problem in the military that negatively influences HRQoL, especially in physical and social functioning. Evaluation of and education on pain were recommended because of body pain and its negative impacts on sleep quality, coping strategies, anxious emotions and HRQoL.

Sex- and age-specific mild cognitive impairment is associated with low hand grip strength in an older Chinese cohort.

BACKGROUND: Few studies have demonstrated the impact of characteristics like age and sex on the association between hand grip strength (HGS) and mild cognitive impairment (MCI). In this cross-sectional study, we aimed to examine the effects of sex and age on the relationship between HGS and MCI. METHODS: We enrolled older adults age ≥60 years (n = 1009) and measured HGS and MCI in all participants. We analyzed the differences in MCI prevalence among the different variables. The role of sex and age in the association between MCI and HGS was analyzed using binary logistic regression. RESULTS: Women had significantly higher prevalence of MCI than men, as did the older group (age ≥70 years) compared with the younger group (age 60-70 years). In men, the low and middle HGS tertiles were significantly associated with MCI. In contrast, only the low tertile of HGS was associated with MCI in women. In the older group, the low tertile of HGS was significantly associated with MCI, which was not observed in the younger group. CONCLUSIONS: HGS was associated with MCI in older adults, and this association was stronger in men. HGS may be useful for evaluating MCI in older adults.

Combined invariants to similarity transformation and to blur using orthogonal Zernike moments.

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.

Fast computation of Tchebichef moments for binary and grayscale images.

Discrete orthogonal moments have been recently introduced in the field of image analysis. It was shown that they have better image representation capability than the continuous orthogonal moments. One problem concerning the use of moments as feature descriptors is the high computational cost, which may limit their application to the problems where the online computation is required. In this paper, we present a new approach for fast computation of the 2-D Tchebichef moments. By deriving some properties of Tchebichef polynomials, and using the image block representation for binary images and intensity slice representation for grayscale images, a fast algorithm is proposed for computing the moments of binary and grayscale images. The theoretical analysis shows that the computational complexity of the proposed method depends upon the number of blocks of the image, thus, it can speed up the computational efficiency as far as the number of blocks is smaller than the image size.