LINC08148 promotes the caveola-mediated endocytosis of Zika virus through upregulating transcription of Src.

Long non-coding RNAs (lncRNAs) represent a new group of host factors involved in viral infection. Current study identified an intergenic lncRNA, LINC08148, as a proviral factor of Zika virus (ZIKV) and Dengue virus 2 (DENV2). Knockout (KO) or silencing of LINC08148 decreases the replication of ZIKV and DENV2. LINC08148 mainly acts at the endocytosis step of ZIKV but at a later stage of DENV2. RNA-seq analysis reveals that LINC08148 knockout downregulates the transcription levels of five endocytosis-related genes including AP2B1, CHMP4C, DNM1, FCHO1, and Src. Among them, loss of Src significantly decreases the uptake of ZIKV. Trans-complementation of Src in the LINC08148KO cells largely restores the caveola-mediated endocytosis of ZIKV, indicating that the proviral effect of LINC08148 is exerted through Src. Finally, LINC08148 upregulates the Src transcription through associating with its transcription factor SP1. This work establishes an essential role of LINC08148 in the ZIKV entry, underscoring a significance of lncRNAs in the viral infection. IMPORTANCE: Long non-coding RNAs (lncRNAs), like proteins, participate in viral infection. However, functions of most lncRNAs remain unknown. In this study, we performed a functional screen based on microarray data and identified a new proviral lncRNA, LINC08148. Then, we uncovered that LINC08148 is involved in the caveola-mediated endocytosis of ZIKV, rather than the classical clathrin-mediated endocytosis. Mechanistically, LINC08148 upregulates the transcription of Src, an initiator of caveola-mediated endocytosis, through binding to its transcription factor SP1. This study identifies a new lncRNA involved in the ZIKV infection, suggesting lncRNAs and cellular proteins are closely linked and cooperate to regulate viral infection.

Development of nanobodies targeting hepatocellular carcinoma and application of nanobody-based CAR-T technology.

BACKGROUND: Chimeric antigen receptor T (CAR-T) cell therapy, as an emerging anti-tumor treatment, has garnered extensive attention in the study of targeted therapy of multiple tumor-associated antigens in hepatocellular carcinoma (HCC). However, the suppressive microenvironment and individual heterogeneity results in downregulation of these antigens in certain patients' cancer cells. Therefore, optimizing CAR-T cell therapy for HCC is imperative. METHODS: In this study, we administered FGFR4-ferritin (FGFR4-HPF) nanoparticles to the alpaca and constructed a phage library of nanobodies (Nbs) derived from alpaca, following which we screened for Nbs targeting FGFR4. Then, we conducted the functional validation of Nbs. Furthermore, we developed Nb-derived CAR-T cells and evaluated their anti-tumor ability against HCC through in vitro and in vivo validation. RESULTS: Our findings demonstrated that we successfully obtained high specificity and high affinity Nbs targeting FGFR4 after screening. And the specificity of Nbs targeting FGFR4 was markedly superior to their binding to other members of the FGFR family proteins. Furthermore, the Nb-derived CAR-T cells, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in both experiments when in vitro and in vivo. CONCLUSIONS: In summary, the results of this study suggest that the CAR-T cells derived from high specificity and high affinity Nbs, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in vitro and in vivo. This is an exploration of FGFR4 in the field of Nb-derived CAR-T cell therapy for HCC, holding promise for enhancing safety and effectiveness in the clinical treatment of HCC in the future.

Comparative study of clinical reasoning competence and self-directed learning competence in nurses across varied years of experience.

OBJECTIVES: To compare and analyse the differences in the clinical reasoning competence of nurses with different working years and their relationship with self-directed learning competence. METHODS: A cross-sectional survey design (online investigation) was used. A total of 376 nurses were recruited from four independent hospitals in China. Online questionnaires collected data on nurses' demographic characteristics and assessed their clinical reasoning and self-directed learning competence. Pearson correlation analysis, t-test, analysis of variance (ANOVA) and multivariate regression analysis were used. RESULTS: Clinical reasoning competence scores of nurses with working years >10 years were higher than those of other nurses. Self-directed learning competence scores of nurses with working years of <1 year and (from ≥1 year to <3 years) were lower than those of nurses with working years of 6-10 years and >10 years. Self-directed learning competence scores of nurses with working years of 3-5 years were lower than those of nurses with working years of >10 years. There was a positive correlation between clinical reasoning competence, self-directed learning competence and each dimension among nurses of different working years. There are differences in the influence of different dimensions of self-directed learning competence on clinical reasoning competence among different working years. CONCLUSION: There were differences in clinical reasoning and self-directed learning competence among nurses with different working years. Self-directed learning competence is a positive predictor of nurses' clinical reasoning competence, which applied to nurses with all working years; however, the specific effect of self-directed learning competence on clinical reasoning competence differed among nurses with different working years. IMPLICATION FOR NURSING MANAGERS: Nursing managers should pay attention to the development characteristics of clinical reasoning competence and self-directed learning competence of nurses with different working years and determine effective intervention strategies according to specific influencing factors.

Machine learning algorithms being an auxiliary tool to predict the overall survival of patients with renal cell carcinoma using the SEER database.

Background: The clinical prognosis assessment of renal cell carcinoma (RCC) still relies on nuclear grading and nuclear score by naked eye with microscope, which has defects long time, low efficiency, and uneven evaluation level criteria. There are few machine learning (ML) studies investigating the prognosis in the RCC literature which could also quantify the risk of postoperative recurrence of RCC patients and guide cancer patients to conduct individualized postoperative clinical management. This study evaluated the suitability of ML algorithms for survival prediction in patients with RCC. Methods: A total of 192,912 RCC patients from the Surveillance, Epidemiology, and End Results (SEER) were obtained from 2004 to 2015. Six ML algorithms including support vector machine (SVM), Bayesian method, decision tree, random forest, neural network, and Extreme Gradient Boosting (XGBoost) were applied to predict overall survival (OS) of RCC. Results: Patients from the SEER with a median age of 62 years and the pathological types were clear cell RCC (47.6%), papillary RCC (9.5%), chromophobe RCC (4.0%) and others (4.1%) were collected. In the deleting patients with missing data, the highest accurate model was XGBoost [area under the curve (AUC) 67.0%]. In the deleting patients with missing data and survival time <5 years, the accuracy of random forest, neural network and XGBoost were high, with AUC of 80.8%, 81.5% and 81.8%, respectively. In the only deleting the missing tumor diameter and filling the missing dataset with missForest, the highest accurate model was random forest (AUC: 71.9%). In this study, the overall accuracy of the SVM model was not high, apart from in the population of patients with deleting the missing tumor diameter and survival time <5 years, and filling the missing data with missForest. Random forest, neural network and XGBoost had high accuracy, with AUC of 84.1%, 84.7% and 84.8%, respectively. Conclusions: ML algorithms could be used to predict the prognosis of RCC. It could quantify the recurrence possibility of patients and help more individualized postoperative clinical management. Given the limitations and complexity of datasets, ML may be used as an auxiliary tool to analyze and process larger datasets and complex data.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. METHODS: A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. RESULTS: We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. CONCLUSIONS: The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. RELEVANCE STATEMENT: The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. KEY POINTS: • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Nuclear membrane protein SUN2 promotes replication of flaviviruses through modulating cytoskeleton reorganization mediated by NS1.

Cytoskeleton is extensively recruited by flaviviruses for their infection. In this study, we uncovered an essential role of a nuclear membrane protein, SAD1/UNC84 domain protein 2 (SUN2) linking cytoskeleton and nucleoskeleton in the flavivirus replication. CRISPR/Cas9-mediated knockout of SUN2, but not SUN1, significantly reduces the replication of Zika virus (ZIKV), dengue virus (DENV), and Japanese encephalitis virus (JEV). In contrast, SUN2 does not affect the infection of non-flaviviridae RNA viruses. All three regions of SUN2 are required for its proviral effect. Mechanistically, SUN2 facilitates rearrangement of cytoskeleton and formation of replication organelles induced by viral infection, and hence promotes viral RNA synthesis. SUN2 is required for the interaction between cytoskeleton actin and ZIKV nonstructural protein 1 (NS1). Expression of dominant negative Nesprin-1 and Nesprin-2, which connect SUN2 to cytoskeleton proteins, alleviates the interaction between actin and NS1 and reduces viral replication levels. In a neonatal mouse infection model, SUN2 knockout dramatically alleviates the in vivo ZIKV replication and development of neuropathology. This work elucidates that recruitment of cytoskeleton proteins by flavivirus is coordinated by nuclear membrane proteins SUN2 and Nesprins, providing evidence for a link between nuclear membrane proteins and flavivirus infection.

Analysis of false reasons based on the artificial intelligence RRCART model to identify frozen sections of lymph nodes in breast cancer.

BACKGROUND: Breast cancer is the most common malignant tumor in the world. Intraoperative frozen section of sentinel lymph nodes is an important basis for determining whether axillary lymph node dissection is required for breast cancer surgery. We propose an RRCART model based on a deep-learning network to identify metastases in 2362 frozen sections and count the wrongly identified sections and the associated reasons. The purpose is to summarize the factors that affect the accuracy of the artificial intelligence model and propose corresponding solutions. METHODS: We took the pathological diagnosis of senior pathologists as the gold standard and identified errors. The pathologists and artificial intelligence engineers jointly read the images and heatmaps to determine the locations of the identified errors on sections, and the pathologists found the reasons (false reasons) for the errors. Through NVivo 12 Plus, qualitative analysis of word frequency analysis and nodal analysis was performed on the error reasons, and the top-down error reason framework of "artificial intelligence RRCART model to identify frozen sections of breast cancer lymph nodes" was constructed based on the importance of false reasons. RESULTS: There were 101 incorrectly identified sections in 2362 slides, including 42 false negatives and 59 false positives. Through NVivo 12 Plus software, the error causes were node-coded, and finally, 2 parent nodes (high-frequency error, low-frequency error) and 5 child nodes (section quality, normal lymph node structure, secondary reaction of lymph nodes, micrometastasis, and special growth pattern of tumor) were obtained; among them, the error of highest frequency was that caused by normal lymph node structure, with a total of 45 cases (44.55%), followed by micrometastasis, which occurred in 30 cases (29.70%). CONCLUSIONS: The causes of identification errors in examination of sentinel lymph node frozen sections by artificial intelligence are, in descending order of influence, normal lymph node structure, micrometastases, section quality, special tumor growth patterns and secondary lymph node reactions. In this study, by constructing an artificial intelligence model to identify the error causes of frozen sections of lymph nodes in breast cancer and by analyzing the model in detail, we found that poor quality of slices was the preproblem of many identification errors, which can lead to other errors, such as unclear recognition of lymph node structure by computer. Therefore, we believe that the process of artificial intelligence pathological diagnosis should be optimized, and the quality control of the pathological sections included in the artificial intelligence reading should be carried out first to exclude the influence of poor section quality on the computer model. For cases of micrometastasis, we suggest that by differentiating slices into high- and low-confidence groups, low-confidence micrometastatic slices can be separated for manual identification. The normal lymph node structure can be improved by adding samples and training the model in a targeted manner.

Simultaneous Determination of Transuranium Radionuclides in Urine by Tandem Quadrupole ICP-MS/MS with Mass-Shift Mode Combined with Chemical Separation.

The urine bioassay method for transuranium nuclides (237Np, 239,240,241Pu, 241Am, and 244Cm) is needed to quickly assess the potential internal contamination in emergency situations. However, in the case that the analysis of multiple radionuclides is required in the same sample, time-consuming/tedious sequential analytical procedures using multiple chromatographic separation resins would have to be employed for the separation of every single radionuclide. In this work, a rapid method for the simultaneous determination of transuranium nuclides in urine was developed by using triple quadrupole inductively coupled plasma mass spectrometry (ICP-MS/MS) combined with a single DGA resin column. The chemical behaviors of Np/Pu and Am/Cm on the DGA resin were consistent in 8-10 mol/L HNO3 and 0.005-0.02 mol/L NaNO2 when 242Pu and 243Am were selected as tracers for Np/Pu and Am/Cm yield monitoring. Based on their different reaction rates with O2, 237Np, 239,240,241Pu, 241Am, and 244Cm in the same solution were simultaneously measured by ICP-MS/MS in the same run. The elimination efficiency of 238U+ tailing (7.43 × 10-9), 238U1H16O2+/238U16O2+ (8.11 × 10-8) and cross contamination of 241Pu and 241Am (<1%) were achieved using 10.0 mL/min He-0.3 mL/min O2 even if the eluate was directly measured without any evaporation. The detection limits of transuranium nuclides were at the femtogram level, demonstrating the feasibility of ICP-MS/MS for simultaneous transuranic radionuclides urinalysis. The developed method was validated by analyzing the spiked urine samples.

The relationship between nurses' social network degree centrality and organizational citizenship behavior: The multiple mediating effects of job satisfaction and work engagement.

Background: In nursing care organizations, nurses' social networks affect their behavior and play an important role in nursing practice. This study aimed to explore the relationships among social network degree centrality, job satisfaction, work engagement and organizational citizenship behavior (OCB) among nurses. Methods: A cross-sectional survey design (one-on-one investigation) was used. The study was conducted among 254 nurses working in 10 nursing units in two hospitals in China from November 2019-February 2020. The participants completed a paper questionnaire that measured social network degree centrality, job satisfaction, work engagement and organizational citizenship behavior. A structural equation model (SEM) was used to analyze the mediating effects of job satisfaction and work engagement. SPSS 22.0 and Amos 21.0 software were used, and the significance level was set at 5% for all analyses. Results: Social network degree centrality was positively associated with job satisfaction, work engagement and organizational citizenship behavior, and job satisfaction and work engagement had significant positive relationships with organizational citizenship behavior. In addition, social network degree centrality influenced organizational citizenship behavior through the multiple mediating effects of job satisfaction and work engagement. Conclusions: Improving job satisfaction and work engagement can boost the positive effect of social network degree centrality on OCB.

Circulating Tumor DNA Dynamics as Prognostic Markers in Locally Advanced and Metastatic Esophageal Squamous Cell Carcinoma.

Importance: Esophageal squamous cell carcinoma (ESCC) is a deadly disease with frequent recurrence. There are unmet needs for prognostic biomarkers for dynamically monitoring disease progression and detecting minimal residual disease. Objective: To examine whether circulating tumor DNA is clinically useful as a prognostic biomarker for ESCC recurrence and patient survival. Design, Setting, and Participants: This single-center, population-based cohort study consecutively enrolled 147 patients receiving curative (n = 74) or palliative (n = 73) treatment at the surgery and clinical oncology departments of Queen Mary Hospital in Hong Kong from August 1, 2016, to September 31, 2021. Patients were followed up for 2 years. Plasma samples were collected at different longitudinal time points for a prospective circulating tumor DNA (ctDNA) next-generation sequencing profiling study of 77 actionable genes. Intervention: Patients were treated with up-front surgery, neoadjuvant chemoradiotherapy plus surgery with or without adjuvant therapy, or palliative chemotherapy (CT). Main Outcomes and Measures: Detection of circulating tumor DNA (ctDNA), progression-free survival (PFS), and overall survival (OS). Results: A total of 478 serial plasma samples from 147 patients with locoregional or metastatic ESCC were prospectively analyzed. Among the 74 patients in the curative group (median [range] age, 66 [46-85] years; 56 [76.0%] male), 44 (59.5%) relapsed and 36 (48.6%) died. For patients receiving curative surgical treatment, a high ctDNA level (hazard ratio [HR], 7.84; 95% CI, 1.87-32.97; P = .005) and ctDNA alterations (HR, 5.71; 95% CI, 1.81-17.97; P = .003) at 6 months postoperation were independently associated with poor OS. Among patients receiving neoadjuvant chemoradiotherapy, postneoadjuvant ctDNA alterations were associated with poor PFS (HR, 3.16; 95% CI, 1.17-8.52; P = .02). In the 73 patients in the palliative group (median [range] age, 63 [45-82] years; 63 [86.0%] male), 71 (97.3%) had disease relapse and 68 (93.2%) died. Detectable pre-CT NFE2L2 alterations were independently associated with PFS (HR, 2.99; 95% CI, 1.35-6.61; P = .007) and OS (HR, 28.39; 95% CI, 7.26-111.03; P = 1.52 × 10-6), whereas high ctDNA levels (HR, 2.41; 95% CI, 1.18-4.95; P = .02) and alterations in pre-cycle III ctDNA (HR, 1.99; 95% CI, 1.03-3.85; P = .04) showed weaker associations with PFS. Alterations in pre-CT ctDNA were independently associated with OS (HR, 4.46; 95% CI, 1.86-10.69; P = 7.97 × 10-4). Conclusions and Relevance: The findings of this cohort study indicate that prognostic models incorporating ctDNA features are useful in ESCC. Both ctDNA level and NFE2L2 alterations pre-CT and before cycle III were found to be important prognostic factors in palliative groups, and ctDNA alterations after treatment and at 6 months after surgery may define high-risk groups for recurrence in the curative group. High-risk patients can benefit by a timely switch to the next therapeutic options.

The relationship between resilience and quality of life in advanced cancer survivors: multiple mediating effects of social support and spirituality.

Background: While previous studies have revealed a positive association between resilience and quality of life in advanced cancer survivors, the mechanisms of the relationship is still unclear. This study aimed to explore the relationships between resilience, social support, spirituality, and quality of life and determine the multiple mediation effects of social support and spirituality on the relationship between resilience and quality of life. Methods: With 286 advanced cancer survivors, a cross-sectional, correlational survey was adopted using convenience sampling. Resilience, social support, spirituality, and quality of life were evaluated by self-report questionnaires. The PROCESS macro for SPSS was used to test the multiple mediation model. Results: The scores for resilience, social support, spirituality and quality of life were positively correlated with one another. Resilience was found to be directly impact quality of life. Meanwhile, the relationship between resilience and quality of life was mediated by social support (effect = 0.067, 95% CI [0.019, 0.120]) and by spirituality (effect = 0.221, 95% CI [0.134, 0.332]), respectively, and by these two serially (effect = 0.036, 95% CI [0.015, 0.067]). Conclusion: Social support and spirituality played multiple mediating roles in the relationship between resilience and quality of life. Interventions aimed at increasing resilience, and then boosting social support and spirituality may be beneficial for promoting quality of life of advanced cancer survivors.

Nanobody-derived bispecific CAR-T cell therapy enhances the anti-tumor efficacy of T cell lymphoma treatment.

T cell lymphoma (TCL) is a highly heterogeneous group of diseases with a poor prognosis and low 5-year overall survival rate. The current therapeutic regimens have relatively low efficacy rates. Clinical studies of single-target chimeric antigen receptor T cell (CAR-T cell) therapy in T lymphocytes require large and multiple infusions, increasing the risks and cost of treatment; therefore, optimizing targeted therapy is a way to improve overall prognosis. Despite significant advances in bispecific CAR-T cell therapy to avoid antigen escape in treatment of B cell lymphoma, applying this strategy to TCL requires further investigation. Here, we constructed an alpaca nanobody (Nb) phage library and generated high-affinity and -specificity Nbs targeting CD30 and CD5, respectively. Based on multiple rounds of screening, bispecific NbCD30-CD5-CAR T cells were constructed, and their superior anti-tumor effect against TCL was validated in vitro and in vivo. Our findings demonstrated that Nb-derived bispecific CAR-T cells significantly improved anti-tumor efficacy in TCL treatment compared with single-target CAR-T cells and bispecific single chain variable fragment (scFv)-derived CAR-T cells. Because Nbs are smaller and less immunogenic, the synergistic effect of Nb-based bispecific CAR-T cells may improve their safety and efficacy in future clinical applications.

Association Between Benefit Finding and Self-care Management in Heart Failure Patient-Caregiver Dyads: The Mediating Role of Mutuality.

BACKGROUND: Inadequate self-care management has been reported in patients with heart failure (HF) and their family caregivers. However, evidence on the influencing factors and corresponding action paths for self-care management within a dyadic context is limited. OBJECTIVE: The aim of this study was to examine dyadic associations between benefit finding and self-care management in HF patient-caregiver dyads and the mediating role of mutuality in these associations. METHODS: This cross-sectional study was conducted in China, and a convenience sample of 253 HF patient-caregiver dyads was included in the analysis. Dyadic benefit finding and mutuality, patients' self-care management, and caregivers' contributions to self-care management were measured using self-reported questionnaires. The actor-partner interdependence model and actor-partner interdependence mediation model were adopted to analyze the data. RESULTS: Patients' benefit finding had an actor effect on their own self-care management (ß = 0.134, P < .05) and a partner effect on caregivers' contributions to self-care management (ß = 0.130, P < .05). Similarly, caregivers' benefit finding had an actor effect on their contributions to self-care management (ß = 0.316, P < .01) and a partner effect on patients' self-care management (ß = 0.187, P < .01). Moreover, patients' mutuality completely mediated the actor effect of their benefit finding on self-care management (ß = 0.127; 95% confidence interval, 0.032-0.233), and caregivers' mutuality partially mediated the actor effect of their benefit finding on contributions to self-care management (ß = 0.060; 95% confidence interval, 0.012-0.124). In addition, caregivers' mutuality completely mediated the partner effect of patients' benefit finding on caregivers' contributions to self-care management (ß = 0.036; 95% confidence interval, 0.009-0.081). CONCLUSIONS: The findings revealed the importance of benefit finding and mutuality, 2 modifiable factors positively associated with dyadic HF self-care management. Dyadic interventions targeting on enhancing benefit finding and mutuality should be designed and implemented to improve HF self-care management.

Factors related to the quality of life of family cancer caregivers.

Background: Cancer caregivers directly affect patient health outcomes. To maintain the function and health of caregivers so that patients can receive efficient care, we must pay more attention to caregivers' quality of life in the process of caring for patients. However, the factors influencing caregivers' quality of life are complex. Aim: To assess caregivers' quality of life in the process of caring for cancer patients and to explore the factors associated with it. Design: This was a descriptive correlational study. A self-report questionnaire was used to anonymously collect data from one Chinese cancer hospital. The Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being (FACIT-Sp-12), General Self-efficacy Scale (GSES), Positive and Negative Affect Schedule (PANAS), Connor-Davidson Resilience Scale 10 (CD-RISC-10), 24-item Caregiver Burden Inventory (CBI) and Caregiver Evaluation Questionnaire were used to measure caregivers' spiritual well-being, self-efficacy, affective well-being, resilience, caregiver burden and quality of life. One-way analysis of variance, the Kruskal-Wallis H test and multiple regression analysis were applied to measure the factors influencing caregivers' situations. Setting and participants: A total of 315 caregivers of cancer patients were selected by convenience sampling. All participants were invited to complete the questionnaire through a one-on-one approach. Results: The mean score for caregiver quality of life was 204.62 ± 36.61. After controlling for demographic factors, self-efficacy (ß' = 0.265, p < 0.01), resilience (ß' = 0.287, p < 0.01) and positive affect (ß' = 0.103, p < 0.01) were protective factors for caregivers' quality of life. Negative affect (ß' = -0.217, p < 0.01) and caregiver burden (ß' = -0.219, p < 0.01) were negative factors. Notably, not all of these predictors can predict all dimensions of quality of life. Conclusion: Caregivers' quality of life needs to be further improved. The results of this study may provide clues to help identify factors influencing caregivers' quality of life and implement targeted strategies to improve their quality of life.

Identifying Candidate Genes for Litter Size and Three Morphological Traits in Youzhou Dark Goats Based on Genome-Wide SNP Markers.

This study aimed to reveal the potential genetic basis for litter size, coat colour, black middorsal stripe and skin colour by combining genome-wide association analysis (GWAS) and selection signature analysis and ROH detection within the Youzhou dark (YZD) goat population (n = 206) using the Illumina GoatSNP54 BeadChip. In the GWAS, we identified one SNP (snp54094-scaffold824-899720) on chromosome 11 for litter size, two SNPs on chromosome 26 (snp11508-scaffold142-1990450, SORCS3) and chromosome 12 (snp55048-scaffold842-324525, LOC102187779) for coat colour and one SNP on chromosome 18 (snp56013-scaffold873-22716, TCF25) for the black middorsal stripe. In contrast, no SNPs were identified for skin colour. In selection signature analysis, 295 significant iHS genomic regions with a mean |iHS| score > 2.66, containing selection signatures encompassing 232 candidate genes were detected. In particular, 43 GO terms and one KEGG pathway were significantly enriched in the selected genes, which may contribute to the excellent environmental adaptability and characteristic trait formation during the domestication of YZD goats. In ROH detection, we identified 4446 ROH segments and 282 consensus ROH regions, among which nine common genes overlapped with those detected using the iHS method. Some known candidate genes for economic traits such as reproduction (TSHR, ANGPT4, CENPF, PIBF1, DACH1, DIS3, CHST1, COL4A1, PRKD1 and DNMT3B) and development and growth (TNPO2, IFT80, UCP2, UCP3, GHRHR, SIM1, CCM2L, CTNNA3 and CTNNA1) were revealed by iHS and ROH detection. Overall, this study is limited by the small population size, which affects the results of GWAS to a certain extent. Nevertheless, our findings could provide the first overview of the genetic mechanism underlying these important traits and provide novel insights into the future conservation and utilisation of Chinese goat germplasm resources.

Privacy preserving for AI-based 3D human pose recovery and retargeting.

As an essential research task in artificial intelligence (AI), the estimation of 3D human poses has important application value in virtual reality, medical diagnosis, athlete training and other fields. However, human pose recovery and retargeting require the acquisition of detailed visual data containing private information, which has led to increasing concerns about user privacy and security. Therefore, we build a lightweight framework, called Human Motion Parameters Prediction (HMPP), which can infer the 3D mesh and 3D skeletal joint points of the human body while protecting the privacy of the user. The proposed method successfully reduces or suppresses privacy attributes while ensuring important features to perform human pose estimation. The 2D and 3D joints are used for supervision to improve the interpretability of the model at each stage. In addition, the prediction of the camera's internal parameters is added so that the model can be augmented with projection supervision, thereby using more 2D datasets for training and improving the generalization ability of the model. Finally, the predicted motion parameters are used for 3D reconstruction and motion retargeting. Experiments show that our approach can achieve excellent evaluation results on multiple datasets and avoid inadvertently compromising private and sensitive data.

Relationships between job burnout, ethical climate and organizational citizenship behaviour among registered nurses: A cross-sectional study.

AIM: This study aimed to investigate the levels of nurses' organizational citizenship behaviour and the associations between job burnout and ethical climate with organizational citizenship behaviour. BACKGROUND: Organizational citizenship behaviour improves adverse outcomes led by nursing shortage. However, the associations between three dimensions of job burnout and organizational citizenship behaviour are inconsistent, and little is known about whether ethical climate is related to organizational citizenship behaviour in nurses. METHODS: In this cross-sectional study, 1157 nurses were selected using convenience sampling from April to October 2019. Self-report surveys assessed nurses' organizational citizenship behaviour, emotional exhaustion, depersonalization, personal accomplishment and perceptions of ethical climate. RESULTS: Mean organizational citizenship behaviour was high among nurses. The regression model showed that job burnout and ethical climate explained an additional 38.6% of the variance in organizational citizenship behaviour over and above sociodemographic factors, with 44.9% of the total variance. CONCLUSION: Nurses' organizational citizenship behaviour was at a relatively high level. Depersonalization was negatively associated with organizational citizenship behaviour while personal accomplishment and ethical climate were positively related to organizational citizenship behaviour. Therefore, nurse leaders are encouraged to take measures to help nurses reduce job burnout and create a favourable ethical climate for increasing nurses' organizational citizenship behaviour.

Genome-wide association analysis of nine reproduction and morphological traits in three goat breeds from Southern China.

OBJECTIVE: This study aimed to investigate the significant single nucleotide polymorphisms (SNPs) and genes associated with nine reproduction and morphological traits in three breed populations of Chinese goats. METHODS: The genome-wide association of nine reproduction and morphological traits (litter size, nipple number, wattle, skin color, coat color, black dorsal line, beard, beard length, and hind leg hair) were analyzed in three Chinese native goat breeds (n = 336) using an Illumina Goat SNP50 Beadchip. RESULTS: A total of 17 genome-wide or chromosome-wide significant SNPs associated with one reproduction trait (litter size) and six morphological traits (wattle, coat color, black dorsal line, beard, beard length, and hind leg hair) were identified in three Chinese native goat breeds, and the candidate genes were annotated. The significant SNPs and corresponding putative candidate genes for each trait are as follows: two SNPs located on chromosomes 6 (CSN3) and 24 (TCF4) for litter size trait; two SNPs located on chromosome 9 (KATNA1) and 1 (UBASH3A) for wattle trait; three SNPs located on chromosome 26 (SORCS3), 24 (DYM), and 20 (PDE4D) for coat color trait; two SNPs located on chromosome 18 (TCF25) and 15 (CLMP) for black dorsal line trait; four SNPs located on chromosome 8, 2 (PAX3), 5 (PIK3C2G), and 28 (PLA2G12B and OIT3) for beard trait; one SNP located on chromosome 18 (KCNG4) for beard length trait; three SNPs located on chromosome 17 (GLRB and GRIA2), 28 (PGBD5), and 4 for hind leg hair trait. In contrast, there were no SNPs identified for nipple number and skin color. CONCLUSION: The significant SNPs or genes identified in this study provided novel insights into the genetic mechanism underlying important reproduction and morphological traits of three local goat breeds in Southern China as well as further potential applications for breeding goats.

Identification of CD98 as a Novel Biomarker for HIV-1 Permissiveness and Latent Infection.

Human immunodeficiency virus type 1 (HIV-1) can integrate viral DNA into host cell chromosomes to establish a long-term stable latent reservoir, which is a major obstacle to cure HIV-1 infection. The characteristics of the HIV-1 latent reservoir have not been fully understood. Here, we identified 126 upregulated plasma membrane proteins in HIV-1 latently infected cells by a label-free liquid chromatography-tandem mass spectrometry analysis. The higher levels of CD98 expression in multiple HIV-1 latently infected cell lines and primary CD4+ T cells compared to uninfected cells were further confirmed by quantitative reverse transcription PCR (RT-qPCR) and flow cytometry analyses. In addition, CD98high CD4+ T cells displayed hyper-permissiveness to HIV-1 infection and possessed distinct immune phenotypic profiles associated with Th17 and peripheral follicular T helper (pTFH) characteristics. Notably, the CD98high resting memory CD4+ T cells harbored significantly higher cell-associated viral RNA and intact provirus than CD98low counterparts in HIV-1-infected individuals receiving combined antiretroviral therapy. Furthermore, CD98high CD4+ T cells exhibited a robust proliferative capacity and significantly contributed to the clonal expansion of the HIV-1 latent reservoir. Our study demonstrates that CD98 can be used as a novel biomarker of HIV-1 latently infected cells to indicate the effect of various strategies to reduce the viral reservoir. IMPORTANCE Identification of cellular biomarkers is the crucial challenge to eradicate the HIV-1 latent reservoir. In our study, we identified CD98 as a novel plasma membrane biomarker for HIV-1 permissiveness and latent infection. Importantly, CD98high CD4+ T cells exhibited a hyper-permissiveness to HIV-1 infection and significantly contributed to the clonal expansion of the HIV-1 latent reservoir. CD98 could be targeted to develop therapeutic strategies to reduce the HIV-1 latent reservoir in further research.

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases.

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists' misdiagnosis rate (p < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists' performance was not significant (p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.