[Design and application of a specialized protective skirt for patients undergoing extracorporeal membrane oxygenation].

Extracorporeal membrane oxygenation (ECMO) provides continuous extracorporeal respiratory and circulatory support for patients with severe heart and lung failure, in order to maintain their lives. Currently, ECMO is an advanced organ support technology and its application in the clinical field of critical care is becoming increasingly common. When ECMO is implemented via percutaneous cannulation at the bilateral femoral artery and vein, the traditional patient pants cannot be used, which leads to exposure of privacy, easy catching of cold, and easy contamination of bed sheets and covers during defecation, making the patient uncomfortable and increasing the risk of infection. Changing bed sheets and covers not only increases the workload of nurses, but also easily causes pipeline displacement or slipping. It is inconvenient to observe the patient's bleeding, displacement, or dislodgement of the pipeline at any time when wearing patient pants. To solve the problems, nursing staff in the emergency intensive care unit of Henan Provincial People's Hospital have designed a protective skirt specifically designed for patients undergoing ECMO, which has obtained a National Utility Model Patent of China (patent number: ZL 2020 2 08120022.9). The special protective skirt for patients with ECMO mainly consists of a skirt body, a transparent observation window, a hip support part, and a fecal collection part. The transparent observation window is convenient for the puncture site and pipeline observation. After the hip support part is inflated, the patient can separate the perianal skin and urine and feces to avoid the occurrence of incontinence dermatitis. The fecal collection part can collect urine and feces to keep the bed unit clean. The protective skirt has a simple structure and is easy to wear and take off. While protecting patient privacy and ensuring patient comfort, it can also observe the condition of the pipeline at any time. It is suitable for patients with lower limb catheterization or urinary and fecal incontinence, and has certain clinical application and promotion value.

An individualized immune prognostic signature in nasopharyngeal carcinoma.

BACKGROUND: Immune-related characteristics can serve as reliable prognostic biomarkers in various cancers. Herein, we aimed to construct an individualized immune prognostic signature in nasopharyngeal carcinoma (NPC). METHODS: This study retrospectively included 455 NPC samples and 39 normal healthy nasopharyngeal tissue specimens. Samples from Gene Expression Omnibus (GEO) were obtained as discovery cohort to screen candidate prognostic immune-related gene pairs based on relative expression ordering of the genes. Quantitative real-time reverse transcription-PCR was used to detect the selected genes to construct an immune-related gene pair signature in training cohort, which comprised 118 clinical samples, and was then validated in validation cohort 1, comprising 92 clinical samples, and validation cohort 2, comprising 88 samples from GEO. RESULTS: We identified 26 immune-related gene pairs as prognostic candidates in discovery cohort. A prognostic immune signature comprising 11 immune gene pairs was constructed in training cohort. In validation cohort 1, the immune signature could significantly distinguish patients with high or low risk in terms of progression-free survival (PFS) (hazard ratio [HR] 2.66, 95 % confidence interval (CI) 1.17-6.02, P=0.015) and could serve as an independent prognostic factor for PFS in multivariate analysis (HR 2.66, 95 % CI 1.17-6.02, P=0.019). Similar results were obtained using validation cohort 2, in which PFS was significantly worse in high risk group than in low risk group (HR 3.02, 95 % CI 1.12-8.18, P=0.022). CONCLUSIONS: The constructed immune signature showed promise for estimating prognosis in NPC. It has potential for translation into clinical practice after prospective validation.

Experimental study of rock fracture behavior under direct tension using three-dimensional digital image correlation.

Understanding the mechanical characteristics of rocks when subjected to direct tension is crucial for assessing the stability of rock formations. Within the scope of this research, a series of tests was conducted using Tage tuff to assess the deformation behavior and crack extension of rock under direct tension. The axial, lateral, and shear strain fields as well as crack propagation, localized deformation behavior, and failure mode of the rocks were analyzed using three-dimensional digital image correlation method. The results showed that the axial strain fields on the specimen surface were heterogeneous, with different locations and localization occurring in the pre-peak stage, which was similar to the evolution of shear strain, whereas the lateral strain only showed slight changes. The crack extension direction was inferred, indicating that both tensile and shear stress occurred in the tests. Furthermore, different stress-strain responses were observed for the inside and outside of the localized bands. Then, the surface patterns of specimen failure were scanned and analyzed to assess the failure mode and residual strength of the specimen under direct tensile stress. Finally, the results of direct tension, uniaxial compression, and Brazilian split tests for Tage tuff were compared, and the complete stress-strain curve of uniaxial tension (UT) was simulated using a nonlinear-variable-compliance constitutive equation. This study provides a deeper understanding into the damage behavior of rocks under direct tension.

The multiple mediating effects of self-efficacy and resilience on the relationship between social support and procrastination among vocational college students: a cross-sectional study.

BACKGROUND: Previous research has revealed a negative association between social support and procrastination. However, few studies have investigated the mechanism underlying this relationship among vocational college students. OBJECTIVE: Based on the social cognitive theory, this study was intended to investigate the multiple mediating effects of self-efficacy and resilience on the relationship between social support and procrastination among vocational college students. METHODS: This study employed a cross-sectional design involving a sample of 1,379 students from a vocational college in China. Data were collected using the General Procrastination Scale, the Multidimensional Scale of Perceived Social Support, the General Self-Efficacy Scale, and the Resilience Scale-14. The PROCESS macro for SPSS was used to examine the multiple mediation model. RESULTS: Our findings indicate significant negative correlations between social support, self-efficacy, resilience, and procrastination. The multiple mediation analysis showed that social support did not have a significant direct impact on procrastination. Instead, the relationship between social support and procrastination was fully mediated by self-efficacy (indirect effect: -0.017; 95% CI: -0.032, -0.004) and resilience (indirect effect: -0.047; 95% CI: -0.072, -0.025), and sequentially mediated by both factors (indirect effect: -0.013; 95% CI: -0.020, -0.007). CONCLUSIONS: The results emphasise the importance of enhancing self-efficacy and resilience in initiatives aimed at preventing and intervening in case of procrastination among vocational college students. Additionally, strengthening social support may also be crucial to preventing or reducing procrastination among this population.

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term mortality risk based on the non-contrast computed tomography (NCCT) image features. METHODS: The NCCT image features of ICH in 135 leukemia patients between 2007-2023 were retrospectively extracted using manual assessment and radiomics methods. After multiple imputation of missing laboratory data, univariate logistic regression and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Random forest models were built with comprehensive evaluation and ranking of feature importance. RESULT: 135 and 129 patients were included in the studies for 7-day and short-term prognostic models, respectively. The median age of all enrolled patients was 35 years, and there were 86 male patients (63.7 %). Clinical models (validation: AUC [area under the curve] = 0.78, AUPRC [area under the precision-recall curve] = 0.73; AUC = 0.84, AUPRC = 0.86), radiomics models (validation: AUC = 0.82, AUPRC = 0.78; AUC = 0.75, AUPRC = 0.77), and the combined models (validation: AUC = 0.84, AUPRC = 0.83; AUC = 0.87, AUPRC = 0.89) predicted 7-day and short-term mortality with good predictive efficacy. Clinical decision curve analysis showed that the combined models predicted 7-day and 30-day risk of death would be more beneficial than other models. Shape features contributed significantly more than semantic features in both radiomics models and combined models (93.3 %, 52.1 %, as well as 85.2 %,37.4 %, respectively) for 7-day and 30-day mortality. CONCLUSIONS: Combined models constructed based on NCCT perform well in predicting the risk of 7-day and short-term mortality in ICH patients with leukemia. Shape features extracted by radiomics are important markers for modeling the prognosis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.