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The relation between sensation seeking, aggression and self-confidence with Needle stick and Sharp injuries among nurses

Background One of the most important occupational injuries experienced by nurses is needle sticks. The causes and factors of needle sticks are not fully known. This study was conducted with the aim of investigating the relationship between sensation-seeking, aggressiveness, and self-confidence with needle stick and Sharp injuries among nurses in a children’s and women’s hospital. Methods This cross-sectional study was conducted on 143 nursing personnel of a children’s and women’s hospital in Iran. To collect data, people were asked to complete several questionnaires, including a demographic questionnaire, Arnett questionnaire of Sensation Seeking, aggression characteristics questionnaire (AGQ), and Rosenberg’s self-confidence questionnaire. They were also asked about the number of injuries caused by nurses’ needles and Sharp injuries in the last 12 months. Data analysis was done using SPSS software (version 22). Results The results of this study showed, no relationship was between sensation seeking, aggression, and self-confidence with Needle sticks and Sharp injuries among nurses (P-value>0.05). Age and work experience have the inverse significant relationship with sensation seeking (P-value<0.05). The most common cause of needle sticks and sharp injuries was syringes (52.9%). Conclusions There is no relationship between sensation seeking, aggression, and self-confidence with Needle sticks and Sharp injuries among nurses. Nurses with high age and more work experience show less sensation-seeking. Needle sticks and Sharp injuries in nurses were mostly caused by syringes.

Correction: Nutritional markers and proteome in patients undergoing treatment for pulmonary tuberculosis differ by geographic region

Comparison of early and intermediate-term outcomes between hybrid arch debranching and total arch replacement: A systematic review and meta-analysis of propensity-matched studies

Objectives To systematically review propensity score-matched studies comparing hybrid arch repair (HAR) with total arch replacement (TAR) for aortic arch pathologies, summarizing early outcomes and intermediate-term results. Methods We searched PubMed, Embase, the Cochrane Library, and Google Scholar to April 2024. The primary outcome was in-hospital mortality, evaluated by a random-effects model to calculate the odds ratio (OR). Time-to-event outcomes were synthesized as hazard ratios (HR) using inverse variance method. Results Eight studies comprising 860 patients were included. There was no significant difference in in-hospital mortality between HAR and TAR groups (OR 0.66; 95% CI 0.33–1.31; p = 0.240). HAR was associated with a lower incidence of renal failure (OR 0.51; 95% CI 0.30–0.88; p = 0.020). In the isolated type A aortic dissection (ITAAD) subgroup, HAR showed a non-significant trend toward lower in-hospital mortality (OR 0.66; 95% CI 0.33–1.31, p = 0.24). In mixed degeneration-dissection (MDAD), TAR showed a non-significant trend toward lower risk of permanent neurological dysfunction (PND) (OR 2.84; 95% CI 0.89–9.10; p = 0.080) and a significantly lower three-year re-interventions rate (HR 2.99; 95% CI 1.48–6.04; p < 0.001). Other postoperative complications did not differ significantly: sternal re-entry for hemorrhage (OR 0.55; 95% CI 0.21–1.43; p = 0.220), and tracheostomy (OR 1.08; 95% CI 0.43–2.72; p = 0.870). Conclusions HAR was associated with a lower risk of renal failure. In ITAAD, HAR showed a trend toward lower in-hospital mortality, whereas in MDAD cohorts, TAR showed a significantly lower three-year re-intervention rate. These findings should be interpreted with caution given the small number of studies and underlying heterogeneity. Further observational studies or randomized trials are warranted.

Evaluation of a digital health platform for preventing stroke in the Australian community: Study protocol for a randomized controlled trial – Love Your Brain

Rationale One in four people will have a stroke in their lifetime. Over 80% of strokes are preventable through the management of modifiable risk factors. There is a growing demand from the community for information about how to prevent stroke. The Love Your Brain digital platform comprises an online course (Massive Online Open Course) and text messages to improve stroke knowledge and motivate behaviour change for stroke prevention. Aims To determine the effect of the digital platform vs a control on attendance at a medical practitioner for cardiovascular risk assessment or management, from either a general practitioner or specialist. Methods and design Love Your Brain is a Phase III, prospective, single-blinded three-arm randomized controlled trial. Eligible participants are community-dwelling residents of Australia aged ≥45 years, who communicate in English language, can access internet and a smartphone, and do not have a self-reported history of stroke or major cardiovascular event. Participants are randomised to either receive the online course, text messages, or general information about stroke risk factors via email (control). Online surveys will be conducted at baseline and 12 weeks. Outcomes will be assessed based on intention-to-treat analysis. Self-reported medical visits will be validated using data linkage. Process and economic evaluations will be conducted in parallel to the trial. An independent statistician blinded to group will analyse the data. Study outcomes and sample size The primary outcome is a visit to a medical practitioner for cardiovascular risk assessment or management within 12-weeks of randomization. Secondary outcomes include: (1) knowledge of stroke signs and risk factors; (2) healthy or risk-modifying behaviours; (3) adherence to medications; (4) process evaluation including intervention delivery/implementation and satisfaction; (5) economic evaluation including health care resource use and cost; and (6) adverse events. Assuming 80% power (two-sided α = 0.05) and 40% prevalence in the control group, 894 participants (298 for each of three groups) will be required to detect a 30% relative increase in medical practitioner attendance for cardiovascular risk assessment or management from either a general practitioner or specialist in the intervention groups. Discussion This study will provide evidence for the efficacy of a low-cost digital health intervention (online course or text messages) to reduce the risk of stroke in the community. Trial registration ACTRN12625000124437

Retraction: Green R & D investment, ESG reporting, and corporate green innovation performance

Retraction: An investigation of financial openness, trade openness, gross capital formation, urbanization, financial development, education and energy nexus in BRI: Evidence from the symmetric and asymmetric framework

Bearing fault diagnosis based on Kepler algorithm and attention mechanism

As a crucial component in rotating machinery, bearings are prone to varying degrees of damage in practical application scenarios. Therefore, studying the fault diagnosis of bearings is of great significance. This article proposes the Kepler algorithm to optimize the weights of neural networks and improve the diagnostic accuracy of the model. At the same time, combined with attention mechanisms, the model will focus on useful information, ignore useless information, and efficiently extract key features. Finally, using third-party bearing data and inputting it into the fault diagnosis model, it was verified that Kepler algorithm and attention mechanism can improve the diagnostic accuracy. Meanwhile, the algorithm proposed in this paper was compared with other algorithms to verify its feasibility and superiority.

Visual complexity of dental intake forms and its association with dental treatment outcomes: A retrospective cohort study

Background While many factors influence dental treatment outcomes, the visual characteristics of intake forms—such as the amount of handwriting—remain largely unexplored. Clinical impressions suggest that minimal or excessive form completion may reflect patient engagement or psychological disposition. To examine whether the visual complexity of intake forms, quantified as a “writing ratio,” is associated with treatment prognosis in dental settings. Methods This retrospective cohort study included 813 patients who received a comprehensive dental consultation at Fukuoka Dental College Hospital in 2016. Intake forms were scanned and processed using Python and OpenCV to calculate the writing ratio, defined as the percentage of black pixels in the image. Patients were categorized into tertiles (Low, Mid, High) based on this ratio. Multivariable logistic regression was used to assess associations with poor treatment outcomes (defined as dropout or clinician-initiated discontinuation), adjusting for age, sex, diagnosis, and the experience of the most senior attending dentist. An exploratory scoring system was constructed using key predictors and evaluated via ROC analysis. Results Patients in the Low writing ratio group had a significantly higher risk of poor outcomes compared to the Mid group (adjusted OR = 1.53, 95% CI: 1.07–2.18, p = 0.019). No significant difference was observed between the Mid and High groups. A subgroup of middle-aged females exhibited the highest dropout rate. The exploratory scoring system showed modest discriminative performance (AUC = 0.544). Conclusions Lower visual complexity of intake forms may reflect disengagement or unclear communication and is associated with poorer treatment outcomes. Intake form appearance may serve as an early behavioral indicator and support risk stratification in dental care. Redesigning intake forms to capture both structural and behavioral cues may enhance early clinical assessment and care planning.

Experimental determination of partial charges with electron diffraction

Abstract Atomic partial charges, integral to understanding molecular structure, interactions and reactivity, remain an ambiguous concept lacking a precise quantum-mechanical definition1,2. The accurate determination of atomic partial charges has far-reaching implications in fields such as chemical synthesis, applied materials science and theoretical chemistry, to name a few3. They play essential parts in molecular dynamics simulations, which can act as a computational microscope for chemical processes4. Until now, no general experimental method has quantified the partial charges of individual atoms in a chemical compound. Here we introduce an experimental method that assigns partial charges based on crystal structure determination through electron diffraction, applicable to any crystalline compound. Seamlessly integrated into standard electron crystallography workflows, this approach requires no specialized software or advanced expertise. Furthermore, it is not limited to specific classes of compounds. The versatility of this method is demonstrated by its application to a wide array of compounds, including the antibiotic ciprofloxacin, the amino acids histidine and tyrosine, and the inorganic zeolite ZSM-5. We refer to this new concept as ionic scattering factors modelling. It fosters a more comprehensive and precise understanding of molecular structures, providing opportunities for applications across numerous fields in the chemical and materials sciences.

DiffDesign: Controllable diffusion with meta prior for efficient interior design generation

Interior design is a complex and creative discipline involving aesthetics, functionality, ergonomics, and materials science. Effective solutions must meet diverse requirements, typically producing multiple deliverables such as renderings and design drawings from various perspectives. Consequently, interior design processes are often inefficient and demand significant creativity. With advances in machine learning, generative models have emerged as a promising means of improving efficiency by creating designs from text descriptions or sketches. However, few generative works focus on interior design, leading to substantial discrepancies between outputs and practical needs, such as differences in size, spatial scope, and the lack of controllable generation quality. To address these challenges, we propose DiffDesign, a controllable diffusion model with meta priors for efficient interior design generation. Specifically, we utilize the generative priors of a 2D diffusion model pre-trained on a large image dataset as our rendering backbone. We further guide the denoising process by disentangling cross-attention control over design attributes, such as appearance, pose, and size, and introduce an optimal transfer-based alignment module to enforce view consistency. Simultaneously, we construct an interior design-specific dataset, DesignHelper, consisting of over 400 solutions across more than 15 spatial types and 15 design styles. This dataset helps fine-tune DiffDesign. Extensive experiments conducted on various benchmark datasets demonstrate the effectiveness and robustness of DiffDesign.

Assessing the connectivity value of roadway structures for terrestrial mammals across the Northern Appalachian forest of Vermont

Landscape connectivity is often negatively impacted by road networks that fragment habitat and result in genetic and demographic consequences for wildlife. Existing roadway structures like bridges, culverts, and underpasses can facilitate connectivity and reduce the barrier effect of roads by providing less risky areas for animals to cross. Estimating areas of high wildlife movement near roads is beneficial for prioritizing transportation investments for wildlife. We used an omnidirectional circuit theory approach to model the movements of eight terrestrial mammal species across the state of Vermont, a forested region central to the globally important Northern Appalachian ecoregion. We combined expert-derived landscape resistance surfaces with wildlife occurrence data to develop species-specific connectivity models at statewide (23,873 km2, 30 m resolution) and roadway structure (100 m radius around 5,912 structures, 0.5 m resolution) scales. The flow of animal movement across the landscape, depicted as electrical current density, was highest for forest-obligate species along the forested, mid-elevation foothills of the Green Mountains in central Vermont and lowest in the agricultural Champlain Valley; however, for more urban- and agriculture-adapted species, flow was highest in developed areas and lower elevation valleys. Average current density was highest for black bear (Ursus americanus), and lowest for striped skunk (Mephitis mephitis) at the statewide scale and highest for raccoon (Procyon lotor) and lowest for moose (Alces alces) at the finer structure scale. Results at both scales revealed different patterns of expected animal movement that reflect the relative extent of connectivity. We then scored connectivity for each structure across all species by combining both scales using four different methods to capture a range of management interests. Rankings varied greatly depending on the method used, highlighting the need to clearly articulate objectives when scoring structures or other features in a landscape. Resistance, occupancy, and current maps also indicated the broad importance of intact forest for connectivity and may be particularly important for identifying priority regions for protection under Vermont’s Community Resilience and Biodiversity Protection Act that mandates protecting 50% of the state by 2050.

A fluorescent-protein spin qubit

YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8

In recent years, You Only Look Once (YOLO) models have gradually been applied to medical image object detection tasks due to their good scalability and excellent generalization performance, bringing new perspectives and approaches to this field. However, existing models overlook the impact of numerous consecutive convolutions and the sampling blur caused by bilinear interpolation, resulting in excessive computational costs and insufficient precision in object detection. To address these problems, we propose a YOLOv8-based model using Efficient modulation and dynamic upsampling (YOLO-ED) to detect lung cancer in CT images. Specifically, we incorporate two innovative modules, the Efficient Modulation module and the DySample module, into the YOLOv8 model. The Efficient Modulation module employs a weighted fusion strategy to extract features from input CT images, effectively reducing model parameters and computational overhead. Furthermore, the DySample module is designed to replace the conventional upsampling component in YOLO, thereby mitigating information loss when expanding feature maps. The dynamic bilinear interpolation introduced by this module increases random bias, which helps minimize errors in feature extraction. To validate the effectiveness of YOLO-ED, we compared it with baselines on the LUNG-PET-CT-DX lung cancer diagnosis dataset and the LUNA16 lung nodule dataset. The results show that YOLO-ED significantly improves precision and reduces computational cost on these two datasets, demonstrating its superiority in the detection of medical images.

A robot scheduling method based on rMAPPO for H-beam riveting and welding work cell

The H-beam riveting and welding work cell is an automated unit used for processing H-beams. By coordinating the gripping and welding robots, the work cell achieves processes such as riveting and welding stiffener plates, transforming the H-beam into a stiffened H-beam. In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. The algorithm employs recurrent neural networks to capture and process historical information. Action masking is used to filter out invalid states and actions, while a shared reward mechanism is adopted to balance cooperation efficiency among agents. Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). Experimental results show that, compared with other baseline methods, the rMAPPO-based agent scheduling method can reduce robot waiting times more effectively, demonstrate greater adaptability in handling different riveting and welding tasks, and significantly enhance the manufacturing efficiency of stiffened H-beam.

Retraction: Incentive mechanism of multiple green innovation behaviors of equipment manufacturing enterprises: A managers, green coordination groups and employees perspective

Development of a family-centered intervention to support self-determination in adolescents and young adults with intellectual disability in home environments: Protocol for a multistage mixed methods design

DKCDC: A clustering algorithm focusing on genuine boundary search for regional division

The majority of existing clustering algorithms, including those algorithms that focus on boundary detection, seldom account for the reasonableness and genuineness of boundaries, consequently, it is difficult to obtain well-defined boundary in clustering-based regional division. A novel boundary search Clustering algorithm integrating Direction Centrality with the Distance of K-nearest-neighbor (DKCDC) is proposed, which is capable of achieving well-defined regional boundaries, to resolve the challenges mentioned above. Firstly, the preliminary boundary of clusters are established on the basis of boundary points and initial cluster labels obtained by the Clustering algorithm using the local Direction Centrality (CDC). Secondly, all the boundary points are further processed and discriminated, to detect noise points concealed within the boundaries, which provides the essential basis for achieving more genuine and reliable cluster boundaries and regional identification. In this process, a fusion strategy is adopted, to subdivide the boundary points into true boundaries and false boundaries by combining voting method and distance metric. Thirdly, a regional division result with well-defined boundary is obtained by DKCDC. In the end, by distinguishing genuine from false boundaries using fusion strategy, DKCDC enhances regional boundary demarcation. Experiments on synthetic and UCI datasets show DKCDC improves silhouette coefficient by at least s4.88% over CDC, K-Means, DBSCAN, OPTICS and HDBSCAN, indicating its broad potential for applications in clustering-based regional division.

GDNF receptor GFRα1 is necessary for the maintenance of dopaminergic neurons in the adult substantia nigra

GFRα1 and Ret are the two necessary components of the receptor for GDNF, a neurotrophic factor discovered in the early 1990’s for its ability to support the survival of midbrain dopaminergic neurons, including those in the substantia nigra (SN) that project to the dorsal striatum (dSTR) and degenerate in Parkinson’s Disease. Several GDNF clinical trials have been conducted to date with mixed results. Despite the physiological and clinical importance of this signaling system, whether any of its components are required for the maintenance of adult SN neurons has not yet been elucidated. In this study, we first analyzed postnatal expression patterns of Gfrα1 and Ret in the SN and established that mRNA levels peak at mouse postnatal day 15 (P15), stabilizing after P30. Using Tamoxifen-induced deletion of Gfrα1 at 3 months of age, we found that GFRα1 is required for the maintenance of a subset of adult SN dopaminergic neurons. FluoroGold tracing of SN axons from the dSTR in mutant mice revealed that ablation of GFRα1 preferentially affects the subset of GFRα1-expressing neurons that project to the STR. In addition to the well-known neuroprotective functions of GDNF/GFRα1/RET signaling, our results establish a physiological requirement of the GFRα1 component of this neurotrophic system for the continuous maintenance of SN dopaminergic neurons in the adult brain.

Effects of chewing on postural learning: An experimental pre-post intervention study

In 16 healthy volunteers (age 42–69 years, 8 females) we investigated chewing effects on postural learning. Initially, the Centre of Pressure (CoP) position in bipedal stance was recorded (1 minute) in 4 conditions: Hard support (HS)-Open Eyes (OE), HS-Closed Eyes (CE), Soft Support (SS)-OE, SS-CE. Following 2 minutes of Chewing (C, n = 8 subjects, 4 females) or rhythmic Hand Grip (HG, n = 8 subjects, 4 females), 10 unipedal stance test (1 minute) were performed for 30 minutes in both groups in HS-OE, with a progressive decrease in CoP Velocity and Path Length. Since the 95% Area of body sway decreased only in the HG group, the Length in Function of Surface (LFS, indicative of balance energy expenditure), increased in the HG and remained constant in the C group. Soon after and 5 hours post-training, bipedal stance tests were performed for 8 minutes, in the same order as before. In both groups, the changes in unipedal stance parameters were found persistent 5 hours post-training. In SS-OE condition of bipedal stance, CoP Velocity was reduced and 95% Area increased by postural training, in the HG and C group, respectively. These modifications were significantly correlated to the corresponding changes in unipedal stance and led to a LSF decrease in the C group. In conclusion, the CoP Velocity during unipedal training was not affected by the previous motor activities. Chewing allowed for a larger compliance concerning the extent of CoP oscillation. Postural training in unipedal stance seem to favour the development of modifications in bipedal stance, according to the conditioning activity. Chewing before a postural training promotes a postural strategy characterized by a constant and a lower energy cost in unipedal and bipedal stance, respectively. Further experiments are necessary to verify whether such a change may promote a more secure balance in trained people.

Public perception and changing attitudes toward antidepressants over a decade in social media: Lessons learned from online discussion using artificial intelligence

Background Antidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients’ perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public’s perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline. Methods This study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann–Kendall test to evaluate shifts in sentiments over time. Results We analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster. Conclusions Negative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.