Enhancing cassava grater design: A customer-driven approach using AHP, QFD, and TRIZ integration.

Due to consistent cassava cultivation, small-scale processing centers rely heavily on the cassava grater. However, these machines face stagnation in innovation and design evolution, leading to inefficiencies, limited capacity, and inconsistent output. Adding to these challenges is the competitive global market, demanding a focus on design enhancements. This study employs a multi-faceted approach involving the Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD) and Theory of Inventive Problem Solving (TRIZ) to prioritize customer requirements, propose technically aligned solutions, and offer innovative design options for cassava graters. A total of 10 customer requirements (CR), 21 technical solutions (TS), and 63 innovative design options (IDO) were established and prioritized, aiming for easy adoption by fabricators, engineers, manufacturers, and artisans. Implementing these insights boosts cassava grater efficiency and productivity and significantly advances knowledge. This work presents a thorough scientific framework for product design, empowering local manufacturers to remain viable and relevant in the rapidly changing field of product enhancement.

bio2Byte Tools deployment as a Python package and Galaxy tool to predict protein biophysical properties.

SUMMARY: We introduce a unified Python package for the prediction of protein biophysical properties, streamlining previous tools developed by the Bio2Byte research group. This suite facilitates comprehensive assessments of protein characteristics, incorporating predictors for backbone and sidechain dynamics, local secondary structure propensities, early folding, long disorder, beta-sheet aggregation and FUS-like phase separation. Our package significantly eases the integration and execution of these tools, enhancing accessibility for both computational and experimental researchers. AVAILABILITY AND IMPLEMENTATION: The suite is available on the Python Package Index (PyPI): https://pypi.org/project/b2bTools/ and Bioconda: https://bioconda.github.io/recipes/b2btools/README.html for Linux and macOS systems, with Docker images hosted on Biocontainers: https://quay.io/repository/biocontainers/b2btools?tab=tags&tag=latest and Docker Hub: https://hub.docker.com/u/bio2byte. Online deployments are available on Galaxy Europe: https://usegalaxy.eu/root?tool_id=b2btools_single_sequence and our online server: https://bio2byte.be/b2btools/. The source code can be found at https://bitbucket.org/bio2byte/b2btools_releases. SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.

Enabling AI in Radiology: Evaluation of an AI Deployment Process.

Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remains limited. Drawing on previous research underscoring the need for comprehensive evaluations of real-world AI deployments, this paper explores the challenges and opportunities encountered while procuring and implementing AI solutions for radiology. The paper aims to contribute to a better understanding of the complexities surrounding AI deployments in real-world clinical settings through a process evaluation study.

Impact of life-threatening military incidents during deployments abroad on the relationships between military personnel and their families.

Introduction: The influence of deployments on family relationships has hardly been investigated. Following a recently proposed new research strategy, military personnel with and without deployment-related life-threatening military incidents during deployment were compared. The hypothesis was that partner and family relationships of military personnel who experienced such an event would deteriorate more. Methods: This study included N = 255 military personnel who had a romantic partner (n = 78 of them had children) when deployed to Afghanistan. Of these, n = 68 military personnel experienced a deployment-related critical event during the deployment, n = 187 did not. Partnership quality was assessed using a semi-structured pre- and post-deployment interview. Results: The partner relationships of military personnel who experienced a deployment-related life-threatening military incident during deployment broke up significantly more often. The partner relationships of all military personnel deteriorated significantly, with greater deterioration after deployment in the group who faced such incidents. These results were independent of age, rank or number of previous deployments. In addition, there was a significant deterioration in the relationships between all military personnel and their children with greater deterioration after deployment in the group who faced such incidents. Conclusion: Life-threatening military incidents during a deployment abroad appear to have a considerable influence on the quality and stability of the partner and family relationships of military personnel. These findings can be used to inform the development of specific pre- and post-deployment measures and training.

The Opportunities and Risks of Large Language Models in Mental Health.

Unlabelled: Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

Regulation of negative emotions through directed attention in high-trait-anxious women: Evidence from event-related potentials and eye-tracking.

Abnormal emotional responses in high-trait-anxious (HTA) individuals may be related to the use of emotion regulation strategies. Directed attention is a substrategy of attention deployment, which has been proven to be effective in regulating individual negative emotions. The present study investigated whether HTA women can effectively utilize directed attention to decrease negative emotions. Two studies were conducted using the same directed attention paradigm, with one focusing on event-related potentials (ERPs) and the other utilizing eye-tracking techniques. Participants viewed negative and neutral pictures and rated their negative emotions experienced during viewing. During directed attention, attention was directed towards highly arousing aspects, less arousing aspects of negative pictures, or less arousing aspects of neutral pictures. In study 1, late positive potentials (LPP) were recorded in 26 HTA and 24 low-trait-anxious (LTA) women. In study 2, the latency of first fixation, the proportion of gaze duration and fixations in the specific area were recorded in 27 HTA and 23 LTA women. Both the HTA and LTA groups revealed a decrease in negative emotional ratings and LPP amplitudes when their attention was directed towards the less arousing aspects of negative pictures. Furthermore, in this condition, the HTA group had a shorter latency of first fixation on highly arousing aspects and a higher proportion of gaze duration on less arousing aspects of negative pictures compared to the LTA group. These results indicate that when confronted with negative pictures, HTA women are able to regulate their emotional responses through directed attention, which may be accompanied by attentional vigilance and avoidance tendencies.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing.

BACKGROUND: The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations. METHODS: Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV. RESULTS: Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing. CONCLUSION: Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

Smartphone-based hyperspectral imaging for ice sheet and proglacial applications in South-West Greenland.

Hyperspectral imaging is a valuable analytical technique with significant benefits for environmental monitoring. However, the application of these technologies remains limited, largely by the cost and bulk associated with available instrumentation. This results in a lack of high-resolution data from more challenging and extreme environmental settings, limiting our knowledge and understanding of the effects of climate change in these regions. In this article we challenge these limitations through the application of a low-cost, smartphone-based hyperspectral imaging instrument to measurement and monitoring activities at the Greenland Ice Sheet. Datasets are captured across a variety of supraglacial and proglacial locations covering visible and near infrared wavelengths. Our results are comparable to the existing literature, despite being captured with instrumentation costing over an order of magnitude less than currently available commercial technologies. Practicalities for field deployment are also explored, demonstrating our approach to be a valuable addition to the research field with the potential to improve the availability of datasets from across the cryosphere, unlocking a wealth of data collection opportunities that were hitherto infeasible.

PON1 Status in Relation to Gulf War Illness: Evidence of Gene-Exposure Interactions from a Multisite Case-Control Study of 1990-1991 Gulf War Veterans.

BACKGROUND: Deployment-related neurotoxicant exposures are implicated in the etiology of Gulf War illness (GWI), the multisymptom condition associated with military service in the 1990-1991 Gulf War (GW). A Q/R polymorphism at position 192 of the paraoxonase (PON)-1 enzyme produce PON1192 variants with different capacities for neutralizing specific chemicals, including certain acetylcholinesterase inhibitors. METHODS: We evaluated PON1192 status and GW exposures in 295 GWI cases and 103 GW veteran controls. Multivariable logistic regression determined independent associations of GWI with GW exposures overall and in PON1192 subgroups. Exact logistic regression explored effects of exposure combinations in PON1192 subgroups. RESULTS: Hearing chemical alarms (proxy for possible nerve agent exposure) was associated with GWI only among RR status veterans (OR = 8.60, p = 0.014). Deployment-related skin pesticide use was associated with GWI only among QQ (OR = 3.30, p = 0.010) and QR (OR = 4.22, p < 0.001) status veterans. Exploratory assessments indicated that chemical alarms were associated with GWI in the subgroup of RR status veterans who took pyridostigmine bromide (PB) (exact OR = 19.02, p = 0.009) but not RR veterans who did not take PB (exact OR = 0.97, p = 1.00). Similarly, skin pesticide use was associated with GWI among QQ status veterans who took PB (exact OR = 6.34, p = 0.001) but not QQ veterans who did not take PB (exact OR = 0.59, p = 0.782). CONCLUSION: Study results suggest a complex pattern of PON1192 exposures and exposure-exposure interactions in the development of GWI.

Automated Neuropsychological Assessment Metrics: Normative Reference Values for U. S. Army National Guard Soldiers.

OBJECTIVE: The Automated Neuropsychological Assessment Metrics (ANAM) is a computerized cognitive test system used extensively with military service members. The aim of this study was to develop a nationally representative normative dataset of Army National Guard (ARNG) personnel and to explore potential relationships between ANAM performances and select military service, demographic, and health factors. METHODS: ANAM performance data were collected using standardized procedures from a representative sample of ARNG service members in six U.S. states. Normative performance values, stratified by age, sex, and military occupational category, were calculated for each ANAM subtest and descriptive measures were computed, along with base rates of below-average performance. The effect of demographic (e.g., age, sex, education, race) and military service and health factors (e.g., deployment, job category, history of head injury) was examined. RESULTS: Data from 1,436 ARNG service members (14.3% female) were analyzed, and normative values calculated. Overall, differences in ANAM performance based on demographic, military service, and health factors were small. A total of 8.9% of the sample had scores on two or more subtests that were ≥ 1.3 SD below the mean; this dropped to 1.9% using a more stringent cut point (≥2 SD below the mean). CONCLUSION: The ANAM normative data reported herein ensure that healthcare providers and researchers have access to reference data that more accurately reflect the larger population of ARNG service members. These data support the assessment and management of ARNG service members' health, as well as future ANAM research involving ARNG service members.

Recent methodological advances in federated learning for healthcare.

For healthcare datasets, it is often impossible to combine data samples from multiple sites due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of powerful machine learning algorithms without requiring the pooling of data. Healthcare data have many simultaneous challenges, such as highly siloed data, class imbalance, missing data, distribution shifts, and non-standardized variables, that require new methodologies to address. Federated learning adds significant methodological complexity to conventional centralized machine learning, requiring distributed optimization, communication between nodes, aggregation of models, and redistribution of models. In this systematic review, we consider all papers on Scopus published between January 2015 and February 2023 that describe new federated learning methodologies for addressing challenges with healthcare data. We reviewed 89 papers meeting these criteria. Significant systemic issues were identified throughout the literature, compromising many methodologies reviewed. We give detailed recommendations to help improve methodology development for federated learning in healthcare.

Unveiling the heart of disaster nursing: A qualitative study on motivations, challenges, and lessons from the devastating 2023 Turkey earthquakes.

BACKGROUND: Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses' pivotal roles and experiences in handling such crises have been disregarded. AIM: This qualitative study analyzed nurses' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster-response efforts. METHODS: This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively  sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research. FINDINGS: The analysis exposed five main themes for pre-tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri-task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post-tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases. CONCLUSIONS: Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake-affected areas. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency.

Deterministic Localization for Fully Automatic Operation: A Survey and Experiments.

With the rapid development of fully automatic operation (FAO) and location-based services, the evaluation criteria of average localization accuracy can no longer meet our demands, in favor of deterministic localization. However, most localization researches modeled localization performance function and enhanced it by minimizing average localization root mean square error (RMSE). The performance degradation in a small region was not considered. In this paper, we present a survey of deterministic localization and analyze the relationship between accuracy and certainty. In this paper, two common solutions of localization enhancement are presented and their localization certainties are discussed. Furthermore, we carry out related localization enhancement experiments in rail transit line and analyze their improvement on deterministic localization. The experimental results show that the overall localization performance is improved, while the deterministic localization requires the stricter solution to promote.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

Effectiveness of artificial reefs in enhancing phytoplankton community dynamics: A meta-analysis.

Artificial reefs (ARs) are widespread globally and play a positive role in enhancing fish communities and restoring habitat. However, the effect of ARs on phytoplankton, which are fundamental to the marine food chain, remains inconclusive. Conducting a literature review and meta-analysis, this study investigates how ARs influence phytoplankton community dynamics by comparing the biomass, density, and diversity of phytoplankton between ARs and natural water bodies across varying deployment durations, constituent materials, and climatic zones. The study findings suggest that, overall, ARs enhance the biomass, density, and diversity of phytoplankton communities, with no significant differences observed compared to natural water bodies. The enhancement effect of ARs on phytoplankton communities becomes progressively more pronounced with increasing deployment time, with the overall status of phytoplankton communities being optimal when artificial reefs are deployed for 5 years or longer. Concrete and stone ARs can significantly enhance the biomass and diversity of phytoplankton, respectively. The effect of ARs on phytoplankton diversity is unrelated to climatic zones. However, deploying ARs in temperate waters significantly enhances phytoplankton biomass, while in tropical waters, it significantly reduces phytoplankton density. The research findings provide practical implications for the formulation of artificial reef construction strategies tailored to the characteristics of different aquatic ecosystems, emphasizing the need for long-term deployment and appropriate material selection. This study offers a theoretical basis for optimizing AR design and deployment to achieve maximum ecological benefits.

Effects of attentional deployment training for relieving negative emotion in individuals with subthreshold depression.

OBJECTIVE: As a prodromal stage to major depressive disorder (MDD), subthreshold depression (StD) has a higher prevalence in the population, resulting in a greater healthcare burden. StD individuals' current negative emotion could be moderated by attentional deployment. However, it remains unclear whether attentional deployment training can mitigate subsequent negative emotion in StD individuals. METHODS: Based on 160 participants, we combined decision task (Experiment 1, N = 69), eye-tracking (Experiment 2, N = 40), and EEG (Experiment 3, N = 51) techniques to investigate how one-week attentional deployment (gain-focus, GF) training modulated the emotional processing of negative stimulus and its underlying neural correlates in StD individuals. RESULTS: After one-week GF training, StD individuals significantly reduced the first fixation time and total fixation time on the negative part (missed opportunities) of decision outcome and showed a decrease in emotional sensitivity to missed opportunities. An increase in N1 and decrease in P3 and LPP (late positive potentials) amplitudes, as well as a decrease in alpha oscillation, were observed when StD individuals faced missed opportunities after training. Additionally, the extent of reduction in StD individuals' emotional sensitivity to missed opportunities could be significantly predicted by the degree of decrease in alpha oscillation. CONCLUSION: One-week attentional deployment training could modulate negative emotion in StD individuals and the degree of change in alpha oscillation might act as an objective indicator for the effectiveness of training. SIGNIFICANCE: Our study provides a convenient and effective approach to alleviate the negative emotion of StD individuals.

YOLO-Granada: a lightweight attentioned Yolo for pomegranates fruit detection.

Pomegranate is an important fruit crop that is usually managed manually through experience. Intelligent management systems for pomegranate orchards can improve yields and address labor shortages. Fast and accurate detection of pomegranates is one of the key technologies of this management system, crucial for yield and scientific management. Currently, most solutions use deep learning to achieve pomegranate detection, but deep learning is not effective in detecting small targets and large parameters, and the computation speed is slow; therefore, there is room for improving the pomegranate detection task. Based on the improved You Only Look Once version 5 (YOLOv5) algorithm, a lightweight pomegranate growth period detection algorithm YOLO-Granada is proposed. A lightweight ShuffleNetv2 network is used as the backbone to extract pomegranate features. Using grouped convolution reduces the computational effort of ordinary convolution, and using channel shuffle increases the interaction between different channels. In addition, the attention mechanism can help the neural network suppress less significant features in the channels or space, and the Convolutional Block Attention Module attention mechanism can improve the effect of attention and optimize the object detection accuracy by using the contribution factor of weights. The average accuracy of the improved network reaches 0.922. It is only less than 1% lower than the original YOLOv5s model (0.929) but brings a speed increase and a compression of the model size. and the detection speed is 17.3% faster than the original network. The parameters, floating-point operations, and model size of this network are compressed to 54.7%, 51.3%, and 56.3% of the original network, respectively. In addition, the algorithm detects 8.66 images per second, achieving real-time results. In this study, the Nihui convolutional neural network framework was further utilized to develop an Android-based application for real-time pomegranate detection. The method provides a more accurate and lightweight solution for intelligent management devices in pomegranate orchards, which can provide a reference for the design of neural networks in agricultural applications.

Sutureless Valves, a "Wireless" Option for Patients With Aortic Valve Disease: JACC State-of-the-Art Review.

Transcatheter technologies triggered the recent revision of the guidelines that progressively widened the indications for the treatment of aortic stenosis. On the surgical realm, a technology avoiding the need for sutures to anchor the prosthesis to the aortic annulus has been developed with the aim to reduce the duration of cardiopulmonary bypass and simplify the process of valve implantation. In addition to a transcatheter aortic valve replacement (TAVR)-like stent that exerts a radial force, these so-called "rapid deployment valves" or "sutureless valves" for aortic valve replacement also have cuffs to improve sealing and reduce the risk of paravalvular leak. Despite promising, the actual advantage of sutureless valves over traditional surgical procedures (surgical aortic valve replacement) or TAVR is still debated. This review summarizes the current comparative evidence reporting outcomes of "sutureless valves" for aortic valve replacement to TAVR and surgical aortic valve replacement in the treatment of aortic valve stenosis.

Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network.

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92, recall was 0.89, accuracy was 0.90, and mAP was 0.91, surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects.

Effects of percutaneous endovascular angioplasty for severe stenosis or occlusion of subclavian artery.

To investigate the effect and safety of percutaneous endovascular angioplasty (PEA) with optional stenting for the treatment of severe stenosis or occlusion of subclavian artery, patients with severe stenosis ≥ 70% or occlusion of subclavian artery treated with PEA were retrospectively enrolled. The clinical data were analyzed. A total of 222 patients were retrospectively enrolled, including 151 males (68.0%) and 71 females (32.0%) aged 48-86 (mean 63.9 ± 9.0) years. Forty-seven (21.2%) patients had comorbidities. Subclavian artery stenosis ≥ 70% was present in 201 (90.5%) patients and complete subclavian occlusion in 21 (9.5%) cases. Angioplasty was successfully performed in all (100%) patients. Balloon-expandable stents were used in 190 (85.6%) cases, and self-expandable stents in 20 (9.0%) cases. Only 12 (5.4%) cases were treated with balloon dilation only. Among 210 patients treated with stent angioplasty, 71 (33.8% or 71/210) cases underwent balloon pre-dilation, 139 (66.2% or 139/210) had direct deployment of balloon-expandable stents, and 2 (1.0% or 2/210) experienced balloon post-dilation. Distal embolization protection devices were used in 5 (2.3% or 5/222) cases. Periprocedural complications occurred in 3 (1.4%) patients, including aortic dissection in 2 (0.9%) cases and right middle cerebral artery embolism in 1 (0.5%). No hemorrhage occurred. Among 182 (82.0%) patients with 6-month follow-up, restenosis > 70% occurred in 1 (0.5%) patient, and among 68 (30.6%) patients with 12-month follow-up, restenosis > 70% took place in 11 (16.2%) patients. Percutaneous endovascular angioplasty can be safely and efficiently performed for the treatment of severe stenosis ≥ 70% or occlusion of subclavian artery.