Nursing adherence, barriers and facilitators to conduct post-stroke dysphagia screening and assessment: A study based on theoretical domain framework.

BACKGROUND: There are an increasing number of evidence-based recommendations for managing dysphagia in post-stroke patients. However, it is unclear whether nurses adopt these recommendations in their daily nursing practices. AIMS: This study aimed to explore nurses' adherence, barriers, facilitators and views on dysphagia screening and assessment of post-stroke dysphagia. METHODS: In this study, multiple methods were adopted. In Phase 1, a general information questionnaire and a knowledge-attitude-practice and barriers/facilitators questionnaire for dysphagia screening and assessment were distributed in 55 hospitals online. In Phase 2, semi-structured interviews were conducted to explore nurses' views on barriers. Descriptive and one-way variance analyses were used to analyse the quantitative data, while content analysis was used to analyse the qualitative data. This study adheres to STROBE and COREQ guidelines. RESULTS: Nine hundred and forty-two completed questionnaires were collected. Only 36.52% of the nurses screened for swallow function in patients as a guideline. The biggest barrier was 'memory, attention and decision process', with an average score of 3.22 (.74). The different stages of implementation had various types and degrees of barriers (p < .001). Five themes were extracted after interviews, namely 'Inadequate environment and resource support', 'Increased workload', 'Professional value perception', 'Organisational culture', and 'Poor knowledge and skill'. CONCLUSIONS: Nurses' practice of dysphagia screening and assessment of patients with dysphagia after stroke were inadequate, and the barriers originated from patients, leadership and the nurses themselves. RELEVANCE TO CLINICAL PRACTICE: This research extracted five barriers of guidance adherence for post-stroke dysphagia screening and assessment and identified the different kinds and degrees of barriers in five implementation stages, providing a basis for nursing managers to break through the bottleneck of guideline implementation. PATIENT OR PUBLIC CONTRIBUTION: The nurses recruited in this study completed validated questionnaires in the survey and suggestive answers in interviews.

Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos).

BACKGROUND AND AIMS: White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS: Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS: Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS: Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).

Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos).

BACKGROUND AND AIMS: Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning-based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE). METHODS: M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice. RESULTS: A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice. CONCLUSIONS: This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.).

Development and psychometric appraisal of Head Nurse Research Leadership Scale.

AIM: To develop a Head Nurse Research Leadership Scale and evaluate its reliability and validity. DESIGN: A psychometric instrument validation study was conducted in two phases. METHODS: The item tool was generated based on a literature review, semi-structured interview and brainstorming. Twenty experts validated the content of the initial version for two rounds. Thirty-nine clinical nurses conducted the HNRLS-v3 to test the readability of the items in pilot study I. Items were screened based on the critical ratio, correlation coefficient analysis, Cronbach's α coefficient and factor analysis using the data collected from 265 nurses in pilot study II. A cross-sectional survey was conducted in six hospitals to evaluate the reliability and validity between 4 January 2022 and 15 January 2022. Three hundred and sixteen nurses participated in this survey, and 60 completed the questionnaire to validate the test-retest reliability between 1 February and 6 February. RESULTS: A 15-item Head Nurse Research Leadership Scale based on 5 dimensions was developed, and the content validity was satisfied. The 15 items accounted for 77.9% of the variance. Confirmatory factor analysis showed acceptable convergent validity and discriminant validity. The Cronbach's α coefficient, split-half reliability and test-retest reliability of the scale were 0.966, 0.9633 and 0.927, respectively.

Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis.

Purpose: To identify latent classes of acute stroke patients with distinct experiences with the symptom clusters of depression, anxiety, fatigue, sleep disturbance, and pain symptoms and assess, if the selected variables determine a symptom-cluster experience in acute stroke patients. Participants and Methods: A sample of 690 participants were collected from July 2020 to December 2020 in a cross-sectional descriptive study. Latent class analysis was conducted to distinguish different clusters of acute stroke participants who experienced five patient-reported symptoms. Furthermore, multinomial logistic regression was selected to verify the influencing indicators of each subgroup, with selected socio-demographic variables, clinical characteristics, self-efficacy, and perceived social support as independent variables and the different latent classes as the dependent variable. Results: Three latent classes, named "all high symptom," "high psychological disorder," and "all low symptom," were identified, accounting for 9.6%, 26.3%, and 64.1% of symptom clusters, respectively. Patients in the "all high symptom" and "high psychological disorder" classes reported significantly lower quality of life (F=40.21, p <0.05). Female gender, younger age, higher National Institutes of Health Stroke Scale scores, and lower self-efficacy and perceived social support were risk factors associated with the "high psychological disorder" class. Younger patients with lower self-efficacy and perceived social support were more likely to be in the "all high symptom" class. Conclusion: This study identified latent classes of acute stroke patients that can be used in predicting symptom-cluster experiences following a stroke. Also, the ability to characterize subgroups of patients with distinct symptom experiences helps identify high-risk patients. Focusing on symptom clusters in clinical practice can inspire us to create effective targeted interventions for subgroups of stroke patients suffering from the same symptom cluster.

Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy.

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.

Silencing of the long non-coding RNA RHPN1-AS1 suppresses the epithelial-to-mesenchymal transition and inhibits breast cancer progression.

Breast cancer (BC) is a frequently diagnosed malignancy in women. Increasing evidence implicates mis-expression of the long non-coding RNA (lncRNA) RHPN1 antisense RNA 1 (RHPN1-AS1) in the development of multiple cancer types. However, little is known about the expression pattern and function of lncRNA RHPN1-AS1 in the pathobiology of BC. We evaluated the expression of RHPN1-AS1 in The Cancer Genome Atlas dataset, and analyzed associations between RHPN1-AS1 expression and clinicopathologic features of BC patients. Additionally, we compared the expression of RHPN1-AS1 between BC and breast non-tumor samples via quantitative real-time polymerase chain reaction, and in situ hybridization, and evaluated the prognostic value of RHPN1-AS1 in a BC tissue microarray. We examined the impact of RHPN1-AS1 knockdown on proliferation, migration, and invasion of BC cells in vitro, and tumor growth in vivo. Bioinformatics analyses were used to predict the function of RHPN1-AS1 in BC. RHPN1-AS1 expression was upregulated in BC and elevated RHPN1-AS1 expression was strongly associated with poor prognosis of BC patients. Moreover, both univariate and multivariate analyses revealed that RHPN1-AS1 was a significant and independent predictor of BC prognosis. Functionally, RHPN1-AS1 silencing attenuated BC cell proliferation, migration, and invasion in vitro, and reduced tumor growth in xenograft models. Furthermore, RHPN1-AS1 silencing was associated with a decrease in the expression of epithelial-to-mesenchymal transition (EMT) markers in the xenograft tumors, suggesting that RHPN1-AS1 promotes invasion in BC cells by enhancing EMT. These findings suggest that RHPN1-AS1 is a potential prognostic biomarker and therapeutic target for BC.

Overexpression of CBX2 in breast cancer promotes tumor progression through the PI3K/AKT signaling pathway.

Breast cancer is the second leading cause of cancer-related death among women worldwide. Emerging evidence suggests that chromobox homolog 2 (CBX2) is overexpressed in breast cancer and plays an essential role in tumor progression. However, its expression and functional roles in breast cancer development and progression require further exploration. Here, we evaluated CBX2 expression in breast cancer using mRNA expression data from the TCGA database; CBX2 expression was upregulated in breast cancer. Furthermore, upregulated CBX2 expression was significantly associated with poorer overall survival (OS) and progression-free survival (PFS) of breast cancer patients. Immunohistochemical analysis of CBX2 expression in a tissue microarray (TMA) cohort yielded concordant results. Univariate and multivariate analyses showed that elevated CBX2 expression was significantly and independently associated with poorer OS of patients in this TMA cohort. Additionally, we performed in vitro functional assays to evaluate the proliferation, migration, and invasion abilities of breast cancer cell lines wherein CBX2 was knocked down using short hairpin RNA (shRNA). CBX2 silencing inhibited cell proliferation, migration, and invasion in vitro. Furthermore, knockdown of CBX2 markedly reduced breast tumorigenesis in xenograft mouse models. Functional and pathway enrichment analyses indicated a positive correlation between high CBX2 expression and activation of the PI3K/AKT pathway, which were further confirmed by western blot and immunohistochemical analyses of mouse tumors. Our findings indicate that CBX2 is a potential prognostic biomarker and therapeutic target for breast cancer.

Psychometric properties of the Chinese version of the Self-Efficacy for Appropriate Medication Use Scale in patients with stroke.

BACKGROUND: It has been reported that stroke has a higher incidence and mortality rate in the People's Republic of China compared to the global average. These conditions can be managed by proper medication use, but ensuring medication adherence is challenging. OBJECTIVE: To translate the Self-Efficacy for Appropriate Medication Use Scale into Chinese and test its validity and reliability in patients with stroke. METHODS: Instrument performances were measured from January 15, 2015 to April 28, 2015 on a convenience sample of 400 patients with stroke recruited at four neurology departments of the First Affiliated Hospital of Zhengzhou University. Questionnaires included the Chinese versions of the Self-Efficacy for Appropriate Medication Use Scale (C-SEAMS) and the General Self-Efficacy Scale (C-GSE). Construct validity, convergent validity, internal consistency, and test-retest reliability were measured. RESULTS: Item analysis showed that item-to-total correlations were in the range of 0.362-0.672. Exploratory factor analysis revealed two factors (which accounted for 60.862% of total variance), with factor loading ranging from 0.534 to 0.756. Confirmatory factor analysis was performed to support the results, with an acceptable fit (χ (2)=73.716; df=64; P<0.01; goodness-of-fit index =0.902; adjusted goodness-of-fit index =0.897; comparative fit index =0.865; root-mean-square error of approximation =0.058). The convergent validity of the C-SEAMS correlated well with the validated measure of the C-GSE in measuring self-efficacy (r=0.531, P<0.01). Good internal consistency (Cronbach's alpha ranged from 0.826 to 0.915) and test-retest reliability (Pearson's correlation coefficient r=0.642, P<0.01) were found. CONCLUSION: The C-SEAMS is a brief and psychometrically sound measure for evaluating self-efficacy for medication adherence in the Chinese population with stroke.