The effects of cognitive behavioral therapy on health-related quality of life, anxiety, depression, illness perception, and in atrial fibrillation patients: a six-month longitudinal study

Abstract Background Atrial fibrillation (AF) often leads to an impaired Health-Related Quality of Life (HRQoL) in many patients. Moreover, psychological factors such as depression, anxiety, and illness perception have been found to significantly correlate with HRQoL. This study aims to evaluate the long-term effectiveness of Cognitive Behavioral Therapy (CBT) in enhancing HRQoL and mitigating psychological distress among AF patients. Methods Employing a prospective, open design with pseudo-randomization, this study encompassed pre-tests, post-treatment evaluations, and a 6-month follow-up. A total of 102 consecutive patients diagnosed with paroxysmal AF were initially enrolled. Out of these, 90 were assigned to two groups; one to receive a 10-week CBT treatment specifically focusing on anxiety, and the other to receive standard care. Outcome measures were evaluated using tools such as the Item Short Form Health Survey (SF-12), General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), University of Toronto Atrial Fibrillation Severity Scale (AFSS), and Brief Illness Perception Questionnaire (BIPQ). These assessments were conducted at pre-treatment, post-treatment, and at the 6-month follow-up mark. We explored the effectiveness of CBT using Generalized Estimating Equations (GEE). Results Our analysis revealed a notable improvement in the CBT group relative to the control group. All metrics displayed consistent improvement across a 6-month duration. At the 6-month checkpoint, the CBT group exhibited a more favorable SF-12 Mental Component Score (MCS) (50.261 ± 0.758 vs. 45.208 ± 0.887, p < 0.001), reduced GAD-7 (4.150 ± 0.347 vs. 8.022 ± 0.423, p < 0.001), BIPQ (34.700 ± 0.432 vs. 38.026 ± 0.318, p < 0.001), and AFSS (9.890 ± 0.217 vs. 10.928 ± 0.218, p = 0.001) scores when compared to the TAU group. Conversely, the SF-12 PCS (44.212 ± 0.816 vs. 47.489 ± 0.960, p = 0.139) and PHQ-9 scores (8.419 ± 0.713 vs. 10.409 ± 0.741, p = 0.794) manifested no significant difference between the two groups. Conclusion The findings suggest that CBT is effective in improving HRQoL and reducing psychological distress among patients with AF at 6 month follow-up. This highlights the potential benefits of integrating CBT into the therapeutic regimen for AF patients. Trial Registration Retrospectively registered with ClinicalTrials.gov (NCT05716828). The date of registration : 5 June 2023

Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence

Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence

A highly stretchable and intrinsically self-healing strain sensor produced by 3D printing

One common issue when implementing wearable strain sensors for health-monitoring is the limited service time when they are inevitably subjected to mechanical operation in practical applications. Therefore, integrating multi-functionality with reliable performance is a long-term pursing target. To this end, we have developed a promising strain sensor utilising a facile 3D printing technology of digital light processing (DLP), thereby simultaneously realising super-high stretchability and intrinsic self-healing ability. Owing to the incorporation of carboxyl multi-walled carbon nanotubes (c-CNTs), the over-curing of the N-acryloylmorpholine (ACMO) resin was adequately mitigated, and good electrical conductivity of the nanocomposite was obtained. On the basis of multi-functionality, the strain sensor before and after self-healing can be applied for real-time and accurate detection of human activities. Therefore, it is expected that the highly stretchable and intrinsically self-healing strain sensor will have promising applications in wearable electronics, personal health care, etc

DataSheet1_An improved approach for the continuous retardation spectra of concrete creep and applications.docx

Creep is an important physical property of concrete and can lead to additional displacement, stress redistribution, and even cracking in concrete structures, inducing prestress loss of large-scale prestressed concrete structures. When an exponential algorithm is used to calculate the long-term creep of concrete, it is usually necessary to apply the continuous retardation spectra of the material. In the improved approach proposed here, the continuous retardation spectra can be obtained by the Weeks inverse Laplace transform. The CEB MC90 creep model is taken as an example to analyze the computational process, efficiency, and error of the approach. The improved approach is further applied to the ACI 209R-92, JSCE, and GL2000 concrete creep models. Through comparison with other methods, the advantages of the improved approach are illustrated, and some useful conclusions are drawn.</p

Table_1_Positive effect of Balint group on burnout and self-efficacy of head nurses in China: a randomized controlled trial.XLSX

BackgroundBurnout is common among nurses and can lead to negative outcomes of medical care. This study aimed to explore the effectiveness of Balint groups to reduce burnout in head nurses in a Chinese hospital.MethodsThis was a randomized controlled trial with a pre- and post-test. A total of 80 head nurses were randomly assigned to either a Balint group (n = 40) or a control group (n = 40). Participants participated in Balint group for a period of 3 months. Participants in both groups completed the Maslach Burnout Inventory-Human Services Survey and the General Self-Efficacy Scale at the beginning and end of the study. Balint group members also completed the Group Climate Questionnaire-Short Form.ResultsIn the Balint group, 33 participants attended all Balint groups, while the 40 participants in the control group had no intervention. Analysis of variance with repeated measures demonstrated a statistically significant difference on the Maslach Burnout Inventory subscale of sense of personal achievement (F = 9.598, p = 0.003) between the Balint and control groups. However, there were no significant differences between the groups on the subscales of emotional exhaustion (F = 0.110, p = 0.740) and depersonalization (F = 0.75, p = 0.387), and the General Self-Efficacy Scale (F = 0.709, p = 0.403).ConclusionsBalint groups helped reduce burnout among head nurses in terms of personal achievement.</p

Swine farm groundwater is a hidden hotspot for antibiotic-resistant pathogenic Acinetobacter

Abstract Acinetobacter is present in the livestock environment, but little is known about their antibiotic resistance and pathogenic species in the farm groundwater. Here we investigated antibiotic resistance of Acinetobacter in the swine farm groundwater (JZPG) and residential groundwater (JZG) of a swine farming village, in comparison to a nearby (3.5 km) non-farming village (WTG) using metagenomic and culture-based approaches. Results showed that the abundance of antibiotic resistome in some JZG and all JZPG (~3.4 copies/16S rRNA gene) was higher than that in WTG (~0.7 copies/16S rRNA gene), indicating the influence of farming activities on both groundwater types. Acinetobacter accounted for ~95.7% of the bacteria in JZG and JZPG, but only ~8.0% in WTG. They were potential hosts of ~95.6% of the resistome in farm affected groundwater, which includes 99 ARG subtypes against 23 antibiotic classes. These ARGs were associated with diverse intrinsic and acquired resistance mechanisms, and the predominant ARGs were tetracyclines and fluoroquinolones resistance genes. Metagenomic binning analysis elucidated that non-baumannii Acinetobacter including A. oleivorans, A. beijerinckii, A. seifertii, A. bereziniae and A. modestus might pose environmental risks because of multidrug resistance, pathogenicity and massive existence in the groundwater. Antibiotic susceptibility tests showed that the isolated strains were resistant to multiple antibiotics including sulfamethoxazole (resistance ratio: 96.2%), levofloxacin (42.5%), gatifloxacin (39.0%), ciprofloxacin (32.6%), tetracycline (32.0%), doxycycline (29.0%) and ampicillin (12.0%) as well as last-resort polymyxin B (31.7%), colistin (24.1%) and tigecycline (4.1%). The findings highlight potential prevalence of groundwater-borne antibiotic-resistant pathogenic Acinetobacter in the livestock environment