搜索结果：Asus

Adequate treatment of acute postoperative pain is one of the quality requirements in ambulatory surgery and its suboptimal management is associated with delayed discharge, unplanned admissions and late admissions after home discharge. The aim of the present study was to learn about the organizational strategy for the management of postoperative pain in ambulatory surgery units (ASU) in Spain. A cross-sectional, multicenter study was carried out based on an electronic survey on aspects related to the management of acute postoperative pain in different ASUs in our country. We recruited 133 ASUs of which 85 responded to the questions on the management of postoperative pain. Of the ASUs that responded, 80% had specific protocols for pain management and 37.6% provided preoperative information on the analgesic plan. The assessment of postoperative pain is carried out in 88.2% of the ASUs in the facility and only 56.5% at home. All ASUs use multimodal analgesia protocols; however, 68.2% report the use of opioids for the treatment of moderate to severe pain. Home invasive analgesia strategies are minimally used by the surveyed ASUs. The DUCMA study highlights that the practice of pain treatment in day surgery remains a challenge in our country and is not always in agreement with national guidelines. The results suggest the need to establish strategies to improve clinical practice and homogenize pain management in ambulatory surgery.

The objective of this work was to evaluate the effects of the replacement of nitrite by natural antioxidants from black garlic (BG) on the quality parameters of jerked beef meat with pork for 60 days. Four formulations were prepared: control, 0.02% of sodium nitrite in brine curing, w/v (CON); 1.5% BG in brine curing, w/v (ASU); 1.5% BG in dry curing, w/w (ASS); and 1.5% of BG in the brine curing, w/v and 1.5% of BG in dry curing, w/w (ASUS). Nutritional composition, pH, water activity, shear force, fatty acid profile, color, and oxidative stability of the formulations were analyzed. The addition of BG did not affect the nutritional composition, pH, water activity, shear force, and fatty acid profile. On the other hand, it resulted in lower weight loss after centrifugation and lower values of L* and a*. TBARS values from the 30th day of storage were lower in the ASUS formulation, while carbonyl compounds at all times were lower than in the CON formulation. Results suggest that BG was an efficient alternative to nitrite in controlling protein oxidation during storage. Thus, the use of pork for the manufacture of jerked beef can be an alternative, and black garlic can be applied as a natural additive to the replacement of nitrite. In addition, black garlic was efficient in improving the oxidative stability of the jerked beef meat with pork.

The objective of this scoping review is to map the evidence on using smartwatches to objectively measure physical activity (PA), sedentary behavior (SB), and sleep in children, adolescents, adults, and older adults. This review followed the Joanna Briggs Institute (JBI) guidelines for scoping reviews, following a previously published protocol. The searches were conducted in January 2022 on Medline, Scopus, Web of Science, IEEE Xplore Digital Library, Scielo, LILACS, Health Technology Assessment Database, Cochrane clinical trials, and clinical trials. The screening was performed independently by two authors. A narrative synthesis was used. After the electronic search, 5,925 records were identified and 2,008 duplicates removed. Screening of 3,917 titles and abstracts resulted in 491 full-text articles assessed for eligibility. A total of 427 studies were excluded for not meeting inclusion criteria, with one study added after author contact. Thus, 64 of these studies were included. Sample sizes ranged widely from 4 to 6,454 participants, with most studies (80.9%) including 100 or fewer participants. Most studies were conducted in the United States (46.9%), followed by China (9.4%). A total of 18 different smartwatch brands were identified, with Apple® being the most investigated (46.9%), followed by Samsung® (15.6), Fitbit® (14.1%), Motorola® (10.9%), Polar® (10.9%), Garmin® (9.4%), Asus® (3.1%), and others (1.6% each. Smartwatches were primarily used to validate measurements of PA, SB, and/or sleep parameters, followed by their use in assessing these variables as exposures or outcomes, testing feasibility, supporting self-monitoring, and other purposes. The most frequently measured variables were steps, total sleep time, and SB. This review can help studies, interventions, and health professionals that aim to use this technology as a measurement instrument, as well as help end users who use it on a daily basis.

Taiwan's strategic focus in digital healthcare has been officially integrated into national industrial policy and identified as a crucial application area for artificial intelligence (AI) and next-generation communication technologies. As the healthcare sector undergoes rapid digital transformation, digital healthcare technologies have emerged as essential tools for improving medical quality and efficiency. Leveraging the extensive coverage of its National Health Insurance (NHI) system and its strengths in Information and Communications Technology (ICT), Taiwan also benefits from the robust research capacity of universities and hospitals. Government-driven regulatory reforms and infrastructure initiatives are further accelerating the advancement of the NHI MediCloud system and the broader digital healthcare ecosystem. This article provides a comprehensive overview of smart healthcare development, highlighting government policy support and the R&D capabilities of universities, research institutes, and hospitals. It also examines the ICT industry's participation in the development of smart healthcare ecosystems, such as Foxconn, Quanta, Acer, ASUS, Wistron, Qisda, etc. With strong data assets, technological expertise, and policy backing, Taiwan demonstrates significant potential in both AI innovation and smart healthcare applications, steadily positioning itself as a key player in the global healthcare market.

We aimed to validate the accuracy of blood pressure (BP) measurement using a smartwatch in patients with acute ischemic stroke. We compared 140 pairs of BP (n = 35) measurements acquired by a smartwatch (ASUS VivoWatch SP) with those measured by a sphygmomanometer (reference device). Differences between the smartwatch BP and reference BP measurements were compared. The validation procedure and criterion followed the consensus of the American National Standards Institute, Inc/Association for the Advancement of Medical Instrumentation/International Organization for Standardization (ANSI/AAMI/ISO) 81060-2:2018 and extended the standard to the specificities of cuffless devices in acute ischemic stroke population. The mean and standard deviation of the differences measured by a smartwatch and reference device were 1.8 ± 5.7 mm Hg in systolic BP and 0.7 ± 3.6 mm Hg in diastolic BP according to criterion 1. The mean and standard deviation of the differences measured by a smartwatch and reference device were 1.8 ± 5.6 mm Hg in systolic BP and 0.7 ± 3.6 mm Hg in diastolic BP according to criterion 2. The results both met the standards of the 2 criterion. The validation result did not differ between the paralytic and non-paralytic arms. The smartwatch with photoplethysmography sensors can provide accurate and reliable measurement of BP in acute ischemic stroke patients.

Ambulatory surgery is a safe and efficient management system to solve surgical problems, but its implementation and development has been variable. The aim of this study is to describe the characteristics, structure and functioning of ambulatory surgery units (ASU) in Spain. Multicenter, cross-sectional, observational study based on an electronic survey, with data collection between April and September 2022. In total, 90 ASUs completed the survey. The mean overall ambulatory index is 63%. More than half of the ASUs (52%) are integrated units. Around half of the units provide training for physicians (51%) and for nurses (55%). The most frequently used quality indicators are suspension rate (87%) and the rate of unplanned admissions (80%). Greater coordination between administrations is needed to obtain reliable data. It is also necessary to implement quality management systems in the different units, as well as to develop tools for the adequate training of the professionals involved.

暂无摘要（点击查看详情）

暂无摘要（点击查看详情）

Low back pain (LBP) is a pervasive global health concern, significantly impacting the quality of life and burdening health care systems. Effective self-management strategies are essential for mitigating the effects of LBP and empowering individuals in their recovery. Digital health interventions, particularly smartphone apps, offer a promising avenue for delivering accessible and personalized self-management support, potentially improving patient adherence to treatment plans. The SelfBack app exemplifies such digital innovations, demonstrating clinical effectiveness in LBP management and prioritizing personalized user experiences. This study describes the comprehensive process of translating and culturally adapting the SelfBack app from English to Arabic for the Saudi context. It further evaluates the perceived usability of the adapted Arabic SelfBack app within a cohort of Saudi individuals experiencing LBP, aiming to ensure accessibility and cultural appropriateness for Arabic-speaking users. A rigorous, 5-stage process was implemented. Stage 1, exchange, focused on adapting the app's core content. English self-assessment scales, used for tailored treatment plans, were replaced with validated, culturally appropriate Arabic versions. Stage 2, translation and cultural adaptation, which translated the content of the patient self-management plan and adapted it for cultural relevance. Stage 3, audio conversion, addressed educational resources. English audio content was professionally translated and rerecorded in Arabic. Stage 4, laboratory usability testing, integrated the Arabic content, verifying interface functionality and right-to-left script compatibility. Stage 5, field usability testing, evaluated the app with 11 Saudi participants experiencing nonspecific LBP using the Arabic System Usability Scale (A-SUS) and semistructured interviews. The translation and adaptation processes are detailed, highlighting the work of an expert panel of linguists, health care professionals, and cultural consultants. The panel identified minimal discrepancies and no significant misunderstandings, demonstrating the accuracy and cultural appropriateness of the adaptation. The mean System Usability Scale (SUS) score was 70%, indicating good usability. Interviews corroborated these results, with participants generally reporting the app as clear, intuitive, and easy to use. However, feedback highlighted areas for improvement, including the perceived number of mandatory questions, a perceived lack of interactivity, repetitive content, and unmet expectations regarding certain functionalities. The Arabic SelfBack app has been successfully developed, demonstrating high user satisfaction, ease of use, and interface efficiency within the target cultural context. The identified criticisms provide actionable insights for future updates. These results suggest that the app is ready for clinical research with Arabic-speaking participants, potentially improving LBP management in Saudi Arabia. Further research will be essential to confirm these initial findings and establish the app's long-term effectiveness.

We present here an overview of the BioCreative VIII Task 3 competition, which called for the development of state-of-the-art approaches to automatic normalization of observations noted by physicians in dysmorphology physical examinations to the Human Phenotype Ontology (HPO). We made available for the task 3136 deidentified and manually annotated observations extracted from electronic health records of 1652 paediatric patients at the Children's Hospital of Philadelphia. This task is challenging due to the discontinuous, overlapping, and descriptive mentions of the observations corresponding to HPO terms, severely limiting the performance of straightforward strict matching approaches. Ultimately, an effective automated solution to the task will facilitate computational analysis that could uncover novel correlations and patterns of observations in patients with rare genetic diseases, enhance our understanding of known genetic conditions, and even identify previously unrecognized conditions. A total of 20 teams registered, and 5 teams submitted their predictions. We summarize the corpus, the competing systems approaches, and their results. The top system used a pre-trained large language model and achieved a 0.82 F1 score, which is close to human performance, confirming the impact that recent advances in natural language processing can have on tasks such as this. The post-evaluation period of the challenge, at https://codalab.lisn.upsaclay.fr/competitions/11351, will be open for submissions for at least 18 months past the end of the competition. Database URL:  https://codalab.lisn.upsaclay.fr/competitions/11351.

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target-RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100-1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices' ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5-1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54-77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry.

This study aims to examine the impact of e-WOM on customer purchase intentions in Facebook fan pages using theories of trust, value co-creation and brand attitude. The present research has set out to explore this emerging domain of study and has thus developed & tested propositions which attempt to establish a relationship between e-WOM and customer's purchase intentions. A deeper understanding of this possible association is obtained by studying the mediating roles of Trust, Value Co-Creation, Brand Image and Brand Attitude. The context for exploring this phenomenon is chosen to be the fan pages of smartphone brands on Facebook. The study involved conducting a sample survey of 490 respondents, comprising of both male and female, who belong to 5 smartphone brands Facebook fan pages-Samsung, Moto G, Lenovo, MI and ASUS are considered for the study. Out of which sample of 100 each has been targeted individually. The findings suggested that e-WOM significantly predicts the purchase intentions of the customers of a specific product and considerable impacted on the purchase decision. The findings of the study also reveal that customer 's trust beliefs, perceived value co-creation, brand image and brand attitude partially mediate in between relationships of e-WOM and purchase intention. The actual presence of different types of consumer electronics brands on the social media, more prominently, the smartphones, which undoubtedly are the most ubiquitous product of this segment. In fact, this indicates that presence on social media is a well- thought organizational strategy developed by companies to gain partial control over the customer 's decision- making process by establishing a close connect with the customers for a long period. This consequence will significantly impact the decision-making process of marketers or practitioners in relation to their marketing tactics. This research also indicates that marketers could devise more effective methods for distributing marketing content through social networking sites, while corporations can cultivate favorable electronic word-of-mouth for their products or services. Through the implementation of social media marketing strategies, companies can increase their sales volume and generate higher revenue. The study examined the role of trust, virtual community participation, and desire to purchase as mediators on smartphone brand fan sites on Facebook. It was observed that these factors had a partial influence on customer purchase intention.

Accurate assessment of wound areas is crucial in making therapeutic decisions, as the prognosis and changes in the size of the wound over time play a significant role. An ideal assessment method should possess qualities such as speed, affordability, accuracy, user-friendliness for both patients and healthcare professionals, and suitability for daily clinical practice. This study aims to introduce a handheld 3-dimensional (3D) scanner and evaluate its accuracy in measuring wound areas. Engineers from the Industrial Technology Research Institute in Taiwan developed a handheld 3D scanner with the intention of extending its application to the medical field. A project was conducted to validate the accuracy of this 3D scanner. We utilized a smartphone (Asus ZenFone 2 with a 13-million-pixel rear camera), a digital single-lens reflex digital camera (Nikon, D5000, Tokyo, Japan), and the 3D scanner to repeatedly measure square papers of known size that were affixed to the curved surface of life-size facial mask or medical teaching breast models. The "Image J" software was employed for 2-dimensional image measurements, while the "3D Edit" software was used to assess the "area of interest" on 3D objects. By using square papers with predetermined dimensions, the measurement-associated error rate (ER) could be calculated for each image. Three repeated measurements were performed using the "Image J" software for each square paper. The ERs of the 3D scan images were all below 3%, with an average ER of 1.64% in this study. The close-up mode of the smartphone exhibited the highest ER. It was observed that as the area increased, the ER also increased in the digital single-lens reflex camera group. The extension distortion effect caused by the wide-angle lens on the smartphone may increase the ER. However, the definition of a healthy skin edge may vary, and different algorithms for calculating the measurement area are employed in various 3D measurement software. Therefore, further validation of their accuracy for medical purposes is necessary. Effective communication with software engineers and discussions on meeting clinical requirements are crucial steps in enhancing the functionality of the 3D scanner.

This paper presents a new hybrid learning and control method that can tune their parameters based on reinforcement learning. In the new proposed method, nonlinear controllers are considered multi-input multi-output functions and then the functions are replaced with SNNs with reinforcement learning algorithms. Dopamine-modulated spike-timing-dependent plasticity (STDP) is used for reinforcement learning and manipulating the synaptic weights between the input and output of neuronal groups (for parameter adjustment). Details of the method are presented and some case studies are done on nonlinear controllers such as Fractional Order PID (FOPID) and Feedback Linearization. The structure and the dynamic equations for learning are presented, and the proposed algorithm is tested on robots and results are compared with other works. Moreover, to demonstrate the effectiveness of SNNFOPID, we conducted rigorous testing on a variety of systems including a two-wheel mobile robot, a double inverted pendulum, and a four-link manipulator robot. The results revealed impressively low errors of 0.01 m, 0.03 rad, and 0.03 rad for each system, respectively. The method is tested on another controller named Feedback Linearization, which provides acceptable results. Results show that the new method has better performance in terms of Integral Absolute Error (IAE) and is highly useful in hardware implementation due to its low energy consumption, high speed, and accuracy. The duration necessary for achieving full and stable proficiency in the control of various robotic systems using SNNFOPD, and SNNFL on an Asus Core i5 system within Simulink's Simscape environment is as follows: - Two-link robot manipulator with SNNFOPID: 19.85656 hours - Two-link robot manipulator with SNNFL: 0.45828 hours - Double inverted pendulum with SNNFOPID: 3.455 hours - Mobile robot with SNNFOPID: 3.71948 hours - Four-link robot manipulator with SNNFOPID: 16.6789 hours. This method can be generalized to other controllers and systems like robots.

Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm ± 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm ± 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm ± 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

Background: Early prediction of sepsis onset is crucial for reducing mortality and the overall cost burden of sepsis treatment. Currently, few effective and accurate prediction tools are available for sepsis. Hence, in this study, we developed an effective sepsis clinical decision support system (S-CDSS) to assist emergency physicians to predict sepsis. Methods: This study included patients who had visited the emergency department (ED) of Taipei Tzu Chi Hospital, Taiwan, between January 1, 2020, and June 31, 2022. The patients were divided into a derivation cohort (n = 70,758) and a validation cohort (n = 27,545). The derivation cohort was subjected to 6-fold stratified cross-validation, reserving 20% of the data (n = 11,793) for model testing. The primary study outcome was a sepsis prediction ( International Classification of Diseases , Tenth Revision , Clinical Modification ) before discharge from the ED. The S-CDSS incorporated the LightGBM algorithm to ensure timely and accurate prediction of sepsis. The validation cohort was subjected to multivariate logistic regression to identify the associations of S-CDSS-based high- and medium-risk alerts with clinical outcomes in the overall patient cohort. For each clinical outcome in high- and medium-risk patients, we calculated the sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy of S-CDSS-based predictions. Results: The S-CDSS was integrated into our hospital information system. The system featured three risk warning labels (red, yellow, and white, indicating high, medium, and low risks, respectively) to alert emergency physicians. The sensitivity and specificity of the S-CDSS in the derivation cohort were 86.9% and 92.5%, respectively. In the validation cohort, high- and medium-risk alerts were significantly associated with all clinical outcomes, exhibiting high prediction specificity for intubation, general ward admission, intensive care unit admission, ED mortality, and in-hospital mortality (93.29%, 97.32%, 94.03%, 93.04%, and 93.97%, respectively). Conclusion: Our findings suggest that the S-CDSS can effectively identify patients with suspected sepsis in the ED. Furthermore, S-CDSS-based predictions appear to be strongly associated with clinical outcomes in patients with sepsis.

The aim of this study was to evaluate the effect of the avocado/soybean unsaponifiables (ASU) in the treatment of induced periodontitis in rats with experimental arthritis. Sixty rats were randomly assigned to 4 groups according to the type of treatment and the systemic condition of the animals: CTR-S: healthy animals in which saline solution (SS) was administered; ASU-S: healthy animals in which ASU (0.6 mg/kg) was administered; AR/ASU-S: animals with induced arthritis in which ASU was administered; AR-S: animals with induced arthritis in which SS was administered. Periodontitis was induced by ligatures, maintained for 15 days. Subsequently, the treatment was performed by scaling with hand instruments. The SS and ASU were administered daily by gavage until euthanasia of the animals that occurred at 7, 15 or 30 days after the scaling procedure (N.=5 animals/group). Bone resorption, inflammatory infiltrate composition, and osteoclastogenesis were assessed. The AR-S group had greater bone loss, smaller amounts of fibroblasts and larger amounts of inflammatory cells than all other groups. In addition, the AR-S group had greater osteoclastogenesis in relation to the healthy animal groups. The use of ASU improved the healing pattern after treatment for experimental periodontitis in animals with arthritis reducing the periodontal bone loss.

Deep learning models have achieved state-of-the-art performance for text classification in the last two decades. However, this has come at the expense of models becoming less understandable, limiting their application scope in high-stakes domains. The increased interest in explainability has resulted in many proposed forms of explanation. Nevertheless, recent studies have shown that rationales, or language explanations, are more intuitive and human-understandable, especially for non-technical stakeholders. This survey provides an overview of the progress the community has achieved thus far in rationalization approaches for text classification. We first describe and compare techniques for producing extractive and abstractive rationales. Next, we present various rationale-annotated data sets that facilitate the training and evaluation of rationalization models. Then, we detail proxy-based and human-grounded metrics to evaluate machine-generated rationales. Finally, we outline current challenges and encourage directions for future work.

We evaluated the use of text-to-image models (Microsoft's Bing Image creator (powered by DALL·E) and Shutterstock's AI image generator) to generate realistic images of human faces and their associated pathology, which may be useful for medical education, given they may overcome issues of patient privacy and requirement for consent. These models have potential to augment rare medical image datasets for medical education, as well as provide greater inclusivity and representation of diverse populations.