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Abstract Introduction/Objective Rapid On-Site Evaluation (ROSE) is an integral part of the fine needle aspiration (FNA) procedure to inform on the diagnostic adequacy of the passes already taken. However, traditional ROSE is not accessible in rural areas and takes valuable cytopathologist time to travel to attend the procedure on site. Telecytology has the promise to allow remote access with equivalent accuracy, though an economic benefit model based on the literature is sparse. Methods/Case Report Mean traditional ROSE times for cytopathologists and mean telecytology ROSE times from two institutions each from the literature were reviewed. The savings by telecytology ROSE was then modeled using the 2019 Medscape Physician Compensation Report (MPCR). Literature review of the nondiagnostic rate both with and without ROSE and costs reported from the University of Pennsylvania were used to model savings from increased accessibility of ROSE to remote settings by telecytology. Results (if a Case Study enter NA) Telecytopathologist traditional ROSE time (20.95 minutes and 56.2 minutes) and telecytology ROSE time (2.91 minutes and 7.5 minutes) was noted. The average pathologist salary per the 2019 MPCR was $120 per hour. The preceding corresponds to monetary savings of $26.90 to $106.58 per case. At one of the reviewed institutions, this would correspond to $23,672 to $93,790.40 in yearly savings. An average reported non- diagnostic rate of 20% (without ROSE with cost of $3,096 per FNA procedure) versus 0.98% (with ROSE with cost of $3327 per procedure) was noted, which if each non-diagnostic FNA results in a repeat procedure, leads to $32,186 in savings per 100 patients. Conclusion Telecytology has promise in not only improving economic efficiency of the ROSE procedure over traditional ROSE, but also in cost savings in remote settings that would otherwise not have access to ROSE at all. Therefore, there are economic savings that could be realized by improving access to telecytology across settings.

AbstractObjectiveTo describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust.DesignData resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital’s new trusted research environment.SettingEHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data.Participants54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England.Main outcome measuresConfirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020.ResultsThe linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one.ConclusionsThis population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.

Health care interventions are increasingly being delivered through digital technologies, offering major opportunities for delivering more health gains from scarce health care resources. Digital health interventions (DHIs) raise distinct challenges for economic evaluations compared with drugs and medical devices, not least due to their interacting, evolving features. The implications of the distinctive nature of DHIs for the methodological choices underpinning their economic evaluation is not well understood. This paper provides an in-depth discussion of distinct features of DHIs and how they might impact the design, measurement, analysis and reporting of cost-effectiveness analysis conducted alongside both randomised and non-randomised studies. These include aspects related to choice of comparator, costs and benefits assessment, study perspective and type of economic analysis. We argue that typical methodological standpoints, such as taking a health service perspective, focusing on health-related benefits and adopting cost-utility analyses, as typically adopted in the economic evaluation of non-digital technologies (pharmaceutical drugs and medical devices), are unlikely to be appropriate for DHIs. We illustrate how these methodological aspects can be appropriately addressed in an evaluation of a digitally supported, remote rehabilitation programme for patients with Long Covid in England. We highlight several methodological considerations for improving practice and areas where further methodological work is required.

Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection.

Remote Access Services (RAS)—including protocols such as Remote Desktop Protocol (RDP), Secure Shell (SSH), Virtual Network Computing (VNC), Telnet, File Transfer Protocol (FTP), and Secure File Transfer Protocol (SFTP)—are essential to modern network infrastructures, particularly with the rise of remote work and cloud adoption. However, their exposure significantly increases the risk of brute-force attacks (BFA), where adversaries systematically guess credentials to gain unauthorized access. Traditional defenses like IP blocklisting and multifactor authentication (MFA) often struggle with scalability and adaptability to distributed attacks. This study introduces a zero-trust-aligned Software-Defined Perimeter (SDP) architecture that integrates Single Packet Authorization (SPA) for service cloaking and Connection Tracking (ConnTrack) for real-time session analysis. A Docker-based prototype was developed and tested, demonstrating no successful BFA attempts observed, latency reduction by above 10% across all evaluated RAS protocols, and the system CPU utilization reduction by 48.7% under attack conditions without impacting normal throughput. It also proved effective against connection-oriented attacks, including port scanning and distributed denial of service (DDoS) attacks. The proposed architecture offers a scalable and efficient security framework by embedding proactive defense at the authentication layer. This work advances zero-trust implementations and delivers practical, low-overhead protection for securing RAS against evolving cyber threats.

Automation is increasingly being introduced into a variety of consumer products, ranging from vacuum cleaners to autonomous vehicles. While automation provides convenience and efficiency benefits consumers value, related evidence suggests it can also undermine post-purchase consumer product responses of importance to managers (e.g., brand loyalty). Using insights derived from Amazon customer reviews, a survey of product owners, a virtual reality lab, and two vignette experiments, we formally explore this possibility and find that automation is indeed a double-edged sword. That is, we uncover that automation has undesirable effects on post-purchase outcomes because it interferes with psychological ownership formation. We also find that, depending on consumer identity motives (e.g., task-related vs. technology-related) and product design affordances (e.g., a remote access feature), this effect can be strengthened, weakened, or even reversed. Our findings offer managers needed guidance on how to counter automation’s dark side through identity-based targeting and product design.

Objectives The robotic retroauricular approach and transoral endoscopic thyroidectomy vestibular approach (TOETVA) have been employed to avoid anterior neck scarring in thyroidectomy with good success. However, outcomes have yet to be compared between techniques. We compare our initial clinical experience with these approaches for thyroid lobectomy at our institution. Methods A review of initial consecutive patients who underwent robotic facelift thyroidectomy (RFT) (August 2011–August 2016) at our institution was conducted. This was compared with the same number of initial consecutive patients who underwent TOETVA (September 2016–September 2017) at our institution. Demographics, operative time, pathology, complications, and learning curve were compared between cohorts. Learning curve was defined based on the slope of linear regression models of operative time versus case number. Results There were 20 patients in each cohort. There was no statistically significant difference in demographic data between cohorts. One hundred percent of RFT cases versus 95% TOETVA cases (P = .999) were completed without conversion to standard open technique with median operative times of 201 (124–293) minutes versus 188 (89–343) minutes with RFT and TOETVA, respectively (P = .36). There was no incidence of permanent recurrent laryngeal nerve injury in either cohort. The slopes of the regression models were 0.29 versus −8.32 (P = .005) for RFT and TOETVA, respectively. Conclusion RFT and TOETVA are safe and feasible options for patients motivated to avoid an anterior neck scar. However, the quicker learning curve without the need for a costly robotic system may make TOETVA the preferred technique for institutions wishing to perform anterior cervical incision‐sparing thyroidectomy. Level of Evidence 4

AimsThe levels of abstraction, vast vocabulary and high cognitive load present significant challenges in undergraduate histopathology education. Self-determination theory describes three psychological needs which promote intrinsic motivation. This paper describes, evaluates and justifies a remotely conducted, post-COVID-19 histopathology placement designed to foster intrinsic motivation.Methods90 fourth-year medical students took part in combined synchronous and asynchronous remote placements integrating virtual microscopy into complete patient narratives through Google Classroom, culminating in remote, simulated multidisciplinary team meeting sessions allowing participants to vote on ‘red flag’ signs and symptoms, investigations, histological diagnoses, staging and management of simulated virtual patients. The placement was designed to foster autonomy, competence and relatedness, generating authenticity, transdisciplinary integration and clinical relevance. A postpositivistic evaluation was undertaken with a validated preplacement and postplacement questionnaire capturing quantitative and qualitative data.ResultsThere was a significant (p<0.001) improvement in interest, confidence and competence in histopathology. Clinical integration and relevance, access to interactive resources and collaborative learning promoted engagement and sustainability post-COVID-19. Barriers to online engagement included participant lack of confidence and self-awareness in front of peers.ConclusionsFostering autonomy, competence and relatedness in post-COVID-19, remote educational designs can promote intrinsic motivation and authentic educational experiences. Ensuring transdisciplinary clinical integration, the appropriate use of novel technology and a focus on patient narratives can underpin the relevance of undergraduate histopathology education. The presentation of normal and diseased tissue in this way can serve as an important mode for the acquisition and application of clinically relevant knowledge expected of graduates.

With the COVID-19 pandemic, the importance of home health care to manage and monitor one’s health status in a home environment became more crucial than ever. This change raised the need for smart home health care services (SHHSs) and their extension to everyday life. However, the factors influencing the acceptance behavior of SHHSs have been inadequately investigated and failed to address why users have the intention to use and adopt the services. This study aimed to analyze the influential factors and measure the behavioral acceptance of SHHSs in South Korea. This study adopted the integrated model of the unified theory of acceptance and use of technology (UTAUT) and task–technology fit (TTF) to understand the behavioral acceptance of SHHSs from users’ perceptions and task–technology fit. Multiple-item scales were established based on validated previous measurement scales and adjusted in accordance with SHHS context. Data from 487 valid samples were analyzed statistically, applying partial least square structural equation modeling. The results indicated that the integrated acceptance model explained 55.2% of the variance in behavioral intention, 44.9% of adoption, and 62.5% of the continuous intention to use SHHSs, supporting 11 of the 13 proposed hypotheses. Behavioral intention was positively influenced by users’ perceptions on performance expectancy, effort expectancy, social influence, and functional conditions. Task–technology fit significantly influenced performance expectancy and behavioral intention, validating the linkage between the two models. Meanwhile, task characteristics were insignificant to determine task–technology fit, which might stem from complex home health care needs due to the COVID-19 pandemic, but were not sufficiently resolved by current service technologies. The findings implied that the acceptance of SHHSs needs to be evaluated according to both the user perceptions of technologies and the matching fit of task and technology. Theoretically, this study supports the applicability of the integrated model of UTAUT and TTF to the domain of SHHS, and newly proposed the measurement items of TTF reflecting the domain specificity of SHHS, providing empirical evidence during the pandemic era in South Korea. Practically, the results could suggest to the planners and strategists of home health care services how to promote SHHS in one’s health management.