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HIV disclosure to family members might be associated with the health-related quality of life (H-QoL) among people living with HIV/AIDS (PLWH). However, previous findings of the association were mixed, and few empirical studies had explored the potential mediating mechanisms underlying the relationship. This study aimed to examine the association between disclosure to family members and H-QoL, and the role of social support in the relationship. A cross-sectional survey was conducted among 1104 PLWH (58.70% male, average age 37.05 years old) in Guangxi, China. Participants provided information on HIV disclosure to family members, perceived social support, H-QoL including physical and mental health dimensions (scored as Physical health score [PHS] and Mental health score [MHS]), and social-demographic characteristics. Descriptive statistics, bivariate analysis, and path analysis were applied to examine our hypotheses. Bivariate analysis demonstrated that HIV disclosure to family members was positively correlated with social support but not with PHS and MHS statistically. Social support was positively correlated with PHS and MHS. Path analysis revealed that the direct effects of disclosure on both physical and mental H-QoL were not significant. In contrast, the indirect effects of disclosure on both mental and physical H-QoL through social support were significant, despite of small effect size. The potential mediating role of social support was highlighted in improving PLWH's well-being. Future interventions targeting H-QoL promotion might incorporate disclosure management and communication skills that are likely to elicit social support and invite patients' family members to participate in the interventions as appropriate., Author(s): Tianyue Mi [sup.1] , Guangyu Zhou [sup.2] , Xiaoming Li [sup.1] , Shan Qiao [sup.1] , Xueying Yang [sup.1] , Zhiyong Shen [sup.3] , Yuejiao Zhou [sup.3] Author Affiliations: [...]

Personal hygiene including wearing facemask and washing hands are instrumental to reduce transmission of COVID-19. The present study applied the health action process approach (HAPA) to examine the process from intention to protective behaviors in the early stages of the COVID-19 pandemic. A longitudinal online survey study was conducted among 229 individuals (61.6% females; M.sub.age = 25.37 years, SD.sub.age = 8.34 years) living in Hubei province, China. Action self-efficacy, outcome expectancy, risk perception, intention, planning and action control regarding facemask wearing and hand washing were assessed at baseline (Time 1), and behaviors were assessed a week later (Time 2). Data were collected from 30 January to 16 February 2020. Two structural equation models were specified to test the theory-driven determinants of the facemask wearing and hand washing respectively. The results showed that action self-efficacy predicted intentions to wear facemasks and wash hands. Intention and action control predicted both behaviors at Time 2. Associations between planning and behaviors were mixed. Mediation analyses revealed that action control significantly mediated the relationship between intention and both behaviors (facemask wearing: 90% CI [0.01, 0.12]; hand washing: 95% CI [0.01, 0.21]). Planning did not mediate the relationship between intention and the two behaviors. The findings illustrate that action self-efficacy is positively associated with intention to facemask wearing and hand washing, and action control contributes to bridging intention to behaviors. Both motivational and volitional factors warrant consideration in interventions to improve adherence to facemask wearing and hand washing in COVID-19., Author(s): Chao Kei Lao [sup.1] , Xinyi Li [sup.1] , Nan Zhao [sup.1] , Mengke Gou [sup.1] , Guangyu Zhou [sup.1] Author Affiliations: (1) grid.11135.37, 0000 0001 2256 9319, School [...]

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein-protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.

BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations.

The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.

This study aimed to investigate whether and how Moloney murine leukemia virus integration site 1 (Bmi-1) plays a role in the regulation of glucose transporter 1 (GLUT1) in gastric adenocarcinoma (GAC). GAC and matched noncancerous tissues were obtained from GAC patients who underwent surgical treatment at the China-Japan Union Hospital, Jilin University (Changchun, Jilin, China). The human GAC cell line AGS and the gastric epithelial cell line GES-1 were used for in vitro studies. BALB/c nude mice were used for in vivo studies. The Bmi-1 and GLUT1 protein levels were significantly greater in human tissues from GAC patients and AGS cells in comparison with controls. Silencing of Bmi-1 resulted in significant decrease in glucose uptake, lactate levels, and GLUT1 expression. In vivo 18F-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging studies indicated that the nude mice bearing xenografts of AGS cells treated with Bmi-1-specific small interfering RNA (siRNA) had a significantly lower maximum standardized uptake value (SUVmax) in comparison with the control mice. Thus, Bmi-1 directly upregulates GLUT1 gene expression, through which it is involved in enhancing glucose uptake in GAC. The results also provide scientific evidence for 18F-FDG PET/CT imaging to evaluate Bmi-1 and glucose uptake in GAC.

Background: Emotional distress, including depressive and anxiety symptoms, is a common concern among pregnant individuals and has negative impacts on maternal and offspring's health. Previous studies indicated the heterogeneity of perinatal emotional distress. Moreover, during the pandemic of COVID-19, expectant mothers are faced with more tough challenges, which could exacerbate their emotional distress. Objective: The aim of present study is to examine potential subgroups with distinct profiles on emotional distress and relationship resources during the pandemic. Methods: A total of 187 pregnant people in China were recruited from April 22 to May 16 in 2020. Latent profile analysis was applied based on prenatal depressive and anxiety symptoms, COVID-19-related negative emotions, prenatal attachment, marital satisfaction and family sense of coherence. Results: Four subgroups were identified. Group 1 and Group 2 shared with low levels of emotional distress and COVID-19-related negative emotions, among which Group 1 had plenty of relationship resources, while Group 2 had insufficient support. Group 3 had moderate levels of emotional distress but above-average prenatal attachment. Group 4 was a highly distressed subtype with severe emotional distress and poor states across all domains. Conclusion: Our findings support that emotion distress among expecting mothers is heterogeneous, highlighting the need for tailed interventions to address the specific needs of subgroups during pregnancy. [ABSTRACT FROM AUTHOR]

Carbon fibre reinforced polymer (CFRP) plays an increasingly important role in many fields, and the non-destructive testing (NDT) for its defects is drawing more and more attention. In this paper, a finite element model is established and the surface temperature difference between the defective and non-defective areas is selected as the characteristic quantity to systematically study the impact of three parameters, such as scanning direction, laser power and scanning speed, on the detection effect in the line laser scanning thermography NDT system. Qualitatively, the scanning direction is consistent with the carbon fibre orientation, the higher the laser power, and the slower the scanning speed is, the more favourable the defect detection. Further, the data obtained from the simulation are fitted, and the results show that the scanning speed and laser power have exponential and linear relationships with the maximum temperature difference, respectively. A related experimental system was established, the laser power was set to 7-, 10-, 13- and 15 W as well as the scanning speed to 10-,20-, 40- and 50 mm/s, to investigate the temperature variation of CFRP specimens containing artificial defects. The experimental results show that a greater laser power or lower scanning speed can obtain better defect detection compared with a smaller power or higher scanning speed. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR) T cell therapy shows promise in treating malignant tumors. However, the use of human epidermal growth factor receptor-2 (HER2) CAR-T cells carries the risk of severe toxicity, including cytokine release syndrome, due to their "on-target off-tumor" recognition of HER2. Enhancing the quality and functionality of HER2 CARs could greatly improve the therapeutic potential of CAR-T cells. In this study, we developed a novel anti-HER2 monoclonal antibody, Ab8, which targets domain III of HER2, distinct from the domain IV recognition of trastuzumab. Although two anti-HER2 mAbs induced similar levels of antibody-dependent cellular cytotoxicity, trastuzumab-based CAR-T cells exhibited potent antitumor activity against HER2-positive cancer cells. In conclusion, our findings provide scientific evidence that antibody recognition of the membrane-proximal domain promotes the anti-tumor response of HER2-specific CAR-T cells. [ABSTRACT FROM AUTHOR]