Determinants of clinical nurses' patient safety competence: a systematic review protocol.

INTRODUCTION: Patient safety has become a fundamental element of healthcare quality. However, despite the ongoing efforts of various organisations, patient safety issues remain a problem in the healthcare system. Given the crucial role of nurses in the healthcare process, improving patient safety competence among clinical nurses is important. In order to promote patient safety competence, it is essential to identify and strengthen the relevant factors. This protocol is for a systematic review aiming to examine and categorise the factors influencing patient safety competence among clinical nurses. METHODS AND ANALYSIS: This review protocol is based on the Joanna Briggs Institute (JBI) Methodology for Systematic Reviews of Effectiveness and Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Four electronic databases, including Ovid-MEDLINE, CINAHL, Cochrane Library and EMBASE, will be used for the systematic review. After consulting with a medical librarian, we designed our search terms to include subject heading terms and related terms in the titles and abstracts. Databases from January 2012 to August 2023 will be searched.Two reviewers will independently conduct the search and extract data including the author(s), country, study design, sample size, clinical setting, clinical experience, tool used to measure patient safety competence and factors affecting patient safety competence. The quality of the included studies will be assessed using the JBI critical appraisal tool. Because heterogeneity of the results is anticipated, the data will be narratively synthesised and divided into two categories: individual and organisational factors. ETHICS AND DISSEMINATION: Ethical review is not relevant to this study. The findings will be presented at professional conferences and published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42023422486.

Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study.

BACKGROUND: Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. OBJECTIVE: The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. METHODS: This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. RESULTS: With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score. CONCLUSIONS: This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning.

INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. OBJECTIVE: This study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. METHOD: To achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. RESULTS: Simulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. CONCLUSION: In light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.

This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.

Lightweight Authentication Scheme for IoT based E-Healthcare Service Communication.

Remote Patient Monitoring (RPM) using Electronic Healthcare (E-health) is a growing phenomenon enabling doctors predict patient health such as possible cardiac arrests from identified abnormal arrythmia. Remote Patient Monitoring enables healthcare staff to notify patients with preventive measures to avoid a medical emergency reducing patient stress. However weak authentication security protocols in IoT wearables such as pacemakers, enable cyberattacks to transmit corrupt data, preventing patients from receiving medical care. In this paper we focus on the security of wearable devices for reliable healthcare services and propose a Lightweight Key Agreement (LKA) based authentication scheme for securing Device-to-Device (D2D) communication. A Network Key Manager on the edge builds keys for each device for device validation. Device authentication requests are verified using certificates, reducing network communication costs. E-health empowered mobile devices are store authentication certificates for future seamless device validation. The LKA scheme is evaluated and compared with existing studies and exhibits reduced operation time for key generation operation costs and lower communication costs incurred during the execution of the device authentication protocol compared with other studies. The LKA scheme further exhibits reduced latency when compared with the three existing schemes due to reduced communication costs.

Secure and Reliable Big-Data-Based Decision Making Using Quantum Approach in IIoT Systems.

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

Federated Learning-Based Secure Electronic Health Record Sharing Scheme in Medical Informatics.

Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.

A Lightweight authentication scheme for IoT against Rogue Base Station Attacks.

Internet of Things (IoT) devices supporting intelligent cloud applications such as healthcare for hospitals rely on connecting with local base stations and access points to provide rich data analysis and real-time services to users. Devices authenticate with local base stations and perform handover operations to connect with access points with higher signal strength. Attackers disguise as valid base stations and access points using publicly accessible SSID information connect with local IoT devices during the handover process and give rise to data integrity and privacy concerns. This paper proposes a lightweight authentication scheme for private blockchain-based networks for securing devices from rogue base stations during the handover process. An authentication certificate is designed for base stations and machines in local clusters using SHA256 and modulo operations for enabling quick handover operations. The keys assigned to each device and base station joining the network are hashed, and their sizes are reduced using modulo operations. Furthermore, the compressed key size forms a certificate, which is used by the machines and the base stations to authenticate mutually. In comparison with existing studies, the performance analysis of the proposed scheme is based on the transmission of three messages required for completing the authentication process. Evaluation based on the Communication Overhead demonstrates a minimum improvement of 99.30% fewer bytes exchanged during the handover process and 89.58% reduced Storage Overhead compared with existing studies.

A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment.

Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks. The delayed detection of botnet formation presents challenges in controlling the spread of malicious scripts in other devices and increases the probability of a high-volume cyberattack. In this paper, we propose a secure Blockchain-enabled Digital Framework for the early detection of Bot formation in a Smart Factory environment. A Digital Twin (DT) is designed for a group of devices on the edge layer to collect device data and inspect packet headers using Deep Learning for connections with external unique IP addresses with open connections. Data are synchronized between the DT and a Packet Auditor (PA) for detecting corrupt device data transmission. Smart Contracts authenticate the DT and PA, ensuring malicious nodes do not participate in data synchronization. Botnet spread is prevented using DT certificate revocation. A comparative analysis of the proposed framework with existing studies demonstrates that the synchronization of data between the DT and PA ensures data integrity for the Botnet detection model training. Data privacy is maintained by inspecting only Packet headers, thereby not requiring the decryption of encrypted data.

Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

CoVAC: A P2P smart contract-based intelligent smart city architecture for vaccine manufacturing.

With the Corona Virus Disease 2019 (COVID-19) outbreak, vaccination is an urgent need worldwide. Internet of Things (IoT) has a vital role in the smart city for vaccine manufacturing with wearable sensors. According to the advanced services in intelligent manufacturing, the fourth resolution is also changing in Industry 5.0 and utilizes high-definition connectivity sensors. Traditional manufacturing companies rely on trusted third parties, which may act as a single point of failure. Access control, big data, and scalability are also challenging issues in existing systems because of the demand response data (DRD) in advanced manufacturing. To mitigate these challenges, CoVAC: A P2P Smart Contract-based Intelligent Smart City Architecture for Vaccine Manufacturing is proposed with three layers, including connection, conversion, and intelligent cloud layer. Smart contract-based blockchain is utilized at the conversion layer for resolving access control, security, and privacy issues. Deep learning is adopted in the intelligent cloud layer for big data analysis and increasing production for vaccine manufacturing in smart city environments. A case study is carried out wherein access data are collected from the various smart plants for vaccines using smart manufacturing to validate the effectiveness of the proposed architecture. Simulation of the proposed architecture is performed on the collected advanced sensor IoT plants data to address the challenges above, offering scalable production in the vaccine manufacturing for the smart city.

Facile Achievement of Complementary Resistive Switching in Block Copolymer Micelle-Based Resistive Memories.

Interest in resistive random access memory (RRAM) has grown rapidly in recent years for realizing ultrahigh density data storage devices. However, sneak currents in these devices can result in misreading of the data, thus limiting the applicability of RRAM. Complementary resistive switching (CRS) memory consisting of two antiserial RRAMs can considerably reduce sneak currents; however, complicated device architectures and manufacturing processes still remain as challenges. Herein, an effective and simple approach for fabricating CRS memory devices using self-assembled block copolymer micelles is reported. Cu ions are selectively placed in the core of polystyrene-block-poly(2-vinylpyridine) spherical micelles, and a hexagonally packed micelle monolayer is prepared through spin-coating. The micelle monolayer can be a symmetrical resistive switching layer, because the micelles and Cu act as dielectric and active metals in memory devices, respectively. The locally enhanced electric field and Joule heating achieved by the structured Cu atoms inside the micelles promote metal ionization and ion migration in a controlled manner, thus allowing for position selectivity during resistive switching. The micelle-based memory device exhibits stable and reliable CRS behavior, with a nonoverlapping and narrow distribution of threshold voltages. Therefore, this approach is promising for fabricating CRS memory devices for high-performance and ultrahigh-density RRAM applications.

Mechanical Properties and Bioactivity of Polyetheretherketone/Hydroxyapatite/Carbon Fiber Composite Prepared by the Mechanofusion Process.

The main obstacles in the melt-processing of hydroxyapatite (HA) and carbon fiber (CF) reinforced polyetheretherketone (PEEK) composite are the high melting temperature of PEEK, poor dispersion of HA nanofillers, and poor processability due to high filler content. In this study, we prepared PEEK/HA/CF ternary composite using two different non-melt blending methods; suspension blending (SUS) in ethanol and mechanofusion process (MF) in dry condition. We compared the mechanical properties and bioactivity of the composite in a spinal cage application in the orthopedic field. Results showed that the PEEK/HA/CF composite made by the MF method exhibited higher flexural and compressive strengths than the composite prepared by the SUS method due to the enhanced dispersibility of HA nanofiller. On the basis of in vitro cell compatibility and cell attachment tests, PEEK/HA/CF composite by mechanofusion process showed an improvement in in vitro bioactivity and osteo-compatibility.

SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City.

Nowadays, the world is experiencing a pandemic crisis due to the spread of COVID-19, a novel coronavirus disease. The contamination rate and death cases are expeditiously increasing. Simultaneously, people are no longer relying on traditional news channels to enlighten themselves about the epidemic situation. Alternately, smart cities citizens are relying more on Social Network Service (SNS) to follow the latest news and information regarding the outbreak, share their opinions, and express their feelings and symptoms. In this paper, we propose an SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Sustainable Healthy City, where Twitter platform is adopted. Over 10000 Tweets were collected during two months, 38% of users aged between 18 and 29, while 26% are between 30 and 49 years old. 56% of them are males and 44% are females. The geospatial location is USA, and the used language is English. Natural Language Processing (NLP) is deployed to filter the tweets. Results demonstrated an outbreak cluster predicted seven days earlier than the confirmed cases with an indicator of 0.989. Analyzing data from SNS platforms enabled predicting future outbreaks several days earlier, and scientifically reduce the infection rate in a smart sustainable healthy city environment.

Astilbe Chinensis ethanol extract suppresses inflammation in macrophages via NF-κB pathway.

BACKGROUND: Macrophages play a crucial role in inflammation. Astilbe chinensis is one of perennial herbs belonging to the genus Astilbe. Plants in the genus have been used for pain, headaches, arthralgia, and chronic bronchitis. However, the effect of A.chinensis on inflammation remains unclear. To study the anti-inflammatory action of A.chinensis ethanol extract (ACE), we investigated the effect of ACE on the production of pro-inflammatory mediators and cytokines in macrophages. METHODS: We evaluated the effectiveness of ACE in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages and thioglycollate (TG)-elicited peritoneal macrophages from male C57BL/6 mice. We measured the levels of pro-inflammatory mediators and cytokines, and examined the anti-inflammatory actions of ACE on nuclear factor κB (NF-κB) pathway in the macrophages. Western blot analysis and immunofluorescence microscopy were used to determine protein level and translocation, respectively. RESULTS: ACE suppressed the output of nitric oxide (NO), prostaglandin E2 (PGE2), and pro-inflammatory cytokines in stimulated macrophages via inhibiting the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins. ACE suppressed mRNA expression of pro-inflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α). We examined the efficacies of ACE on NF-κB activation by measuring the expressions including IκB kinase (IKK), inhibitor of κB (IκB), and nuclear p65 proteins. In addition, the inhibition of NF-κB p65's translocation was determined with immunofluorescence assay. CONCLUSION: Our findings manifested that ACE inhibited LPS or TG-induced inflammation by blocking the NF-κB signaling pathway in macrophages. It indicated that ACE is a potential therapeutic mean for inflammation and related diseases.

SOF System; A New Nomenclature System for the Surgical Techniques of Cervical Spine Deformity.

Although cervical spinal deformity (CSD) can have a profoundly negative impact on an individual's quality of life and there have been many advances in surgical treatment of CSD in recent years, there exists no comprehensive classification system of surgical treatment that categorizes anterior and posterior surgery separately according to the grade of surgery. The objective of this study is to introduce the new classification system of various surgical treatments for CSD. We developed a new classification system (SOF system) for CSD surgery that describes the sequence of surgical approach (S), the grade of osteotomy (O), and the information of fixation (F) using alphanumeric codes. This new classification system can provide a consistent description of the various osteotomies performed in CSD surgery. Especially, regarding research, there has been a clear benefit to this classification. Having a standardized classification that allows for common frame for cervical deformity correction surgery, communication between surgeons and the evaluation of the CSD surgeries make it possible to conduct global comparative research about surgical outcome.

Evaluation and Surgical Planning for Craniovertebral Junction Deformity.

Craniovertebral junction (CVJ) deformity is a challenging pathology that can result in progressive deformity, myelopathy, severe neck pain, and functional disability, such as difficulty swallowing. Surgical management of CVJ deformity is complex for anatomical reasons; given the discreet relationships involved in the surrounding neurovascular structures and intricate biochemical issues, access to this region is relatively difficult. Evaluation of the reducibility, CVJ alignment, and direction of the mechanical compression may determine surgical strategy. If CVJ deformity is reducible, posterior in situ fixation may be a viable solution. If the deformity is rigid and the C1-2 facet is fixed, osteotomy may be necessary to make the C1-2 facet joint reducible. C1-2 facet release with vertical reduction technique could be useful, especially when the C1-2 facet joint is the primary pathology of CVJ kyphotic deformity or basilar invagination. The indications for transoral surgery are becoming as narrow as a treatment for CVJ deformity. In this article, we will discuss CVJ alignment and various strategies for the management of CVJ deformity and possible ways to prevent complications and improve surgical outcomes.

Enhanced Tensile Properties of Multi-Walled Carbon Nanotubes Filled Polyamide 6 Composites Based on Interface Modification and Reactive Extrusion.

To induce uniform dispersion and excellent interfacial properties, we adopted a strategy of combining both polyamide 6 (PA6) grafting for multi-walled carbon nanotubes (MWCNTs) and reactive extrusion of PA6 matrix, based on anionic ring-opening polymerization of ε-caprolactam (CL). Compared to -COOH and -NCO treatments of MWCNTs, enhanced MWCNT dispersion and tensile properties of the composites were achieved using the applied strategy, and the tensile strength and modulus of the PA6-grafted MWCNT-filled PA6 composites were 5.3% and 20.5% higher than those of the purified MWCNT-filled PA6 composites, respectively. In addition, they were almost similar to the theoretical ones calculated by the modified Mori-Tanaka method (MTM) assuming a perfect interface, indicating that the tensile properties of MWCNT-filled PA6 composites can be optimized by PA6 grafting and reactive extrusion based on the anionic ring-opening polymerization of CL due to uniform MWCNT dispersion and excellent interfacial property.

Structure determination and quantification of a new flavone glycoside with anti-acetylcholinesterase activity from the herbs of Elsholtzia ciliata.

Three acacetin triglycosides (compounds 1, 2 and 3) were isolated from the herbs of Elsholtzia ciliata (Labiatae). The structure were identified as 7-O-ß-D-glucopyranosyl-(1 â†’ 2)[α-L-rhamnopyranosyl-(1 â†’ 6)]-ß-D-glucopyranoside (compound 1), 7-O-(6-O-acetyl)-ß-D-glucopyranosyl-(1 â†’ 2)[α-L-rhamnopyranosyl-(1 â†’ 6)]-ß-D-glucopyranoside (compound 2) and 7-O-(6-O-acetyl)-ß-D-glucopyranosyl-(1 â†’ 2)[(4-O-acetyl)-α-L-rhamnopyranosyl-(1 â†’ 6)]-ß-D-glucopyranoside (compound 3) of acacetin. The structures of these compounds were determined on the basis of 2D-NMR spectroscopic data. Compound 3 has not been isolated from a natural source. In addition, the three compounds were quantitatively analysed by HPLC. Acetylcholinesterase (AChE) inhibition activity was assayed to find anti-Alzheimer's activity, since this enzyme increases the concentration of acetylcholine (ACh), a neurotransmitter, responsible for brain's memory. Acacetin, the aglycone of the three compounds, exhibited a potent anti-cholinesterase activity (IC50, 50.33 ± 0.87), though its glycosides (1, 2 and 3) were less active. HPLC analysis demonstrated that the three compounds were contained in the MeOH extract in the order of compounds 2 (12.63 mg/g extract) > 3 (3.10 mg/g) > 1 (2.92 mg/g).