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Abstract Background High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy’s efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. Methods The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; “rest easy”) trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial’s primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children’s hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. Discussion Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. Trial registration ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.

This article examines the integration of OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) into Objective Structured Clinical Examinations (OSCEs) for medical education. OSCEs, essential in evaluating medical trainees, are time and resource-intensive for educators and medical colleges. ChatGPT emerges as a solution, aiding educators in efficient OSCE preparation, including case development, standardized patient training, assessment methods, and grading rubrics. We explore ChatGPT's role in reducing trainee stress through simulated interactions of realistic practice scenarios and real-time trainee feedback. We also discuss the importance of validating ChatGPT outputs for medical accuracy and address compliance concerns. While highlighting ChatGPT's potential in reducing time and cost burdens for educators, we underscore the need for careful and informed application of Artificial Intelligence in medical education. Through examples, we outline ChatGPT's promising future in augmenting medical training and assessment, balancing technological innovation with educational integrity.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Indeed, high yielding and high-grade crop production are crucial in modern agriculture, and smart farming technologies like vertical hydroponic farming can play a significant role in achieving these goals. The proposed vertical hydroponic system appears to be quite advanced and promising. Maintaining a cool and controlled environment is essential for optimal plant growth. Grow lights provide the necessary spectrum and intensity for photosynthesis, enabling plants to grow efficiently. Proper monitoring of water levels and moisture content ensures that plants receive an adequate supply of nutrients, preventing over or under-watering. The use of an Arduino controller with a keypad allows for user inputs and customization of the hydroponic system. Farmers can set parameters for water changes, flow rates, and indoor temperature to tailor the system to specific plant requirements. Monitoring and controlling various environmental parameters like light intensity, pH levels, electrical conductivity, water temperature, and relative humidity are essential for achieving the best possible crop yields and quality. Sensors and actuators help achieve this precision. The Internet of Things (IoT) integration allows for real-time data transfer and retrieval. Hydroponic Hardware prototype is proposed by combining these advanced technologies, the system can create an optimal environment for plant growth while minimizing resource wastage and maximizing yields. It’s worth noting that as technology continues to evolve, there will likely be further advancements in smart farming systems.

ObjectiveChest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia.Materials and methodsWe performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes and logistic regression. We evaluated their performance of each model from a validation sample of 1,350 chest radiographs developed as a stratified random sample of 35% admitted and 65% discharged patients when both using expert consensus and diagnosis codes.ResultsOf 172,662 encounters in the derivation sample, 15.6% had a discharge diagnosis of pneumonia in a primary or secondary position. The median patient age in the derivation sample was 3.7 years (interquartile range, 1.4–9.5 years). In the validation sample, 185/1350 (13.8%) and 205/1350 (15.3%) were classified as pneumonia by content experts and by diagnosis codes, respectively. Compared to content experts, Naïve Bayes had the highest sensitivity (93.5%) and XGBoost had the highest F1 score (72.4). Compared to a diagnosis code of pneumonia, the highest sensitivity was again with the Naïve Bayes (80.1%), and the highest F1 score was with the support vector machine (53.0%).ConclusionNLP algorithms can accurately identify pediatric pneumonia from radiography reports. Following external validation and implementation into the electronic health record, these algorithms can facilitate clinical decision support and inform large database research.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) may impair immune modulating host microRNAs, causing severe disease. Our objectives were to determine the salivary miRNA profile in children with SARS-CoV-2 infection at presentation and compare the expression in those with and without severe outcomes. Children p-value < 0.05). The majority (31/43) were downregulated in severe cases. The largest between-group differences involved miR-4495, miR-296-5p, miR-548ao-3p and miR-1273c. These microRNAs displayed enrichment for 32 gene ontology pathways including viral processing and transforming growth factor beta and Fc-gamma receptor signaling. In conclusion, salivary miRNA levels are perturbed in children with severe COVID-19, with the majority of miRNAs being down regulated. Further studies are required to validate and determine the utility of salivary miRNAs as biomarkers of severe COVID-19.

This study aims to analyse the mediating and moderating roles played by tacit and explicit knowledge in the relationship between organisational capabilities and product innovation. On a sample of industrial organisations, SEM and multigroup analysis have been applied. Our findings show that both explicit and tacit knowledge partially mediate the relationship between organisational capabilities and product innovation, being stronger the mediating role played by tacit knowledge. While high levels of tacit knowledge enhance the impact of organisational capabilities on explicit knowledge and product innovation, a moderate level of explicit knowledge maximises the impact on the relationships between organisational capabilities, tacit knowledge, and product innovation. The discussion of the results, the managerial implications, and future research lines are included at the end of the paper. [ABSTRACT FROM AUTHOR]

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this report is to provide insight from pediatric stakeholders with a shared desire to facilitate a revision of the current United States regulatory pathways for the development of pediatric healthcare devices. Methods: On August 5, 2020, a group of innovators, engineers, professors and clinicians met to discuss challenges and opportunities for the development of new medical devices for pediatric health and the importance of creating a regulatory environment that encourages and accelerates the research and development of such devices. On January 6, 2021, this group joined regulatory experts at a follow-up meeting. Results: One of the primary issues identified was the need to present decision-makers with opportunities that change the return-on-investment balance between adult and pediatric devices to promote investment in pediatric devices. Discussion/Conclusion: Several proposed strategies were discussed, and these strategies can be divided into two broad categories: 1. Removal of real and perceived barriers to pediatric device innovation; 2. Increasing incentives for pediatric device innovation.

Gear is a reliable part of power transmission. This article presents a metallurgical failure analysis of a failed gear used in automobile engine coolant pumps. In gear, the soft microstructure of pearlite and ferrite is witnessed in metallographic analysis. Significant wear is perceived in gear teeth, and significant deformation is observed in the keyway. The crack initiated from the corner radius of the keyway and propagated towards to the root of the teeth, which is evidence of the failure due to fatigue. Recommendations are given to improve the fatigue strength of gear.

Doping of graphene with variety of heteroatoms is presently focused for electrochemical supercapacitor applications because it functions as a hybrid supercapacitor. Herewith, we report the fabrication of Se‐doped graphene with selenium dioxide using a supercritical fluid technique. Both the existence as well as the quantity of Se‐doping in graphene are established using XPS and EDAX techniques. The Se‐doped graphene derived from the weight ratio of selenium dioxide and graphene oxide of 1 : 3 resulted in an enriched gravimetric capacitance achieving 581 F g−1 (1 M H2SO4). The specific capacity of the optimised Se‐doped graphene based symmetric supercapacitor in 0.01 M solution of ammonium metavanadate in 1 M H2SO4 solution is found to be 420 C g−1 and attained an improved capacitance retention of 85% over 50000 cycles along with 94% coulombic efficiency. The energy density of the Se‐doped graphene symmetric supercapacitor is found to be 23.33 Wh kg−1 in 0.01 M solution of ammonium metavanadate in 1 M H2SO4 solution. [ABSTRACT FROM AUTHOR]