Layman's Terms Research Articles

Clinician's Guide to Epitranscriptomics: An Example of N1-Methyladenosine (m1A) RNA Modification and Cancer.

Epitranscriptomics is the study of modifications of RNA molecules by small molecular residues, such as the methyl (-CH3) group. These modifications are inheritable and reversible. A specific group of enzymes called "writers" introduces the change to the RNA; "erasers" delete it, while "readers" stimulate a downstream effect. Epitranscriptomic changes are present in every type of organism from single-celled ones to plants and animals and are a key to normal development as well as pathologic processes. Oncology is a fast-paced field, where a better understanding of tumor biology and (epi)genetics is necessary to provide new therapeutic targets and better clinical outcomes. Recently, changes to the epitranscriptome have been shown to be drivers of tumorigenesis, biomarkers, and means of predicting outcomes, as well as potential therapeutic targets. In this review, we aimed to give a concise overview of epitranscriptomics in the context of neoplastic disease with a focus on N1-methyladenosine (m1A) modification, in layman's terms, to bring closer this omics to clinicians and their future clinical practice.

Exploring Patient Preferences Related to Shared Decision-Making in Chronic Disease Management.

Background and objective Shared decision-making (SDM) in healthcare has transitioned from a paternalistic model to a collaborative approach, particularly significant in chronic disease management. This shift focuses on aligning healthcare decisions with patient preferences and values, thereby enhancing patient engagement and treatment adherence. However, patient preferences regarding involvement in SDM vary widely, influenced by demographic, disease-specific, psychological, cultural, and social factors. This study aimed to explore patient preferences related to SDM in chronic disease management in Saudi Arabia, by assessing attitudes towardSDM, the impact of decision aids, and the role of clinician communication in influencing these preferences. Methods A cross-sectional survey design was employed, involving 409 adult outpatients with chronic diseases attending four public hospitals in Saudi Arabia. Participants were selected using purposive and convenience sampling. The survey, translated into Arabic, collected demographic data and information on preferences and experiences in decision-making, communication, and information sharing. The data were analyzed using SPSS Statistics (IBM Corp., Armonk, NY) to identify patterns and correlations. Results Key findings indicated a strong preference among the participants for involvement in treatment decisions (n=303, 74.2%) and clear communication using layman's terms (n=338, 82.6%). Major barriers to active participation in SDM included lack of time during appointments (n=275, 67.2%), difficulty understanding medical terminology (n=220, 53.9%), and feeling intimidated to ask questions (297, 72.6%).Comfort in SDM was highest intheage group of 41-50 years[mean=4.16, standard deviation (SD)=28.44; F=2.3287, p=0.0739]. Patient satisfaction was significantly higher in the age group of 18-30 years(mean=3.42, SD=1.09; F=3.0503, p=0.0284). Conclusions Our findings highlightthe need for incorporating patient preferences into chronic disease management strategies to enhance engagement and satisfaction.

AI-Driven Patient Education in Chronic Kidney Disease: Evaluating Chatbot Responses against Clinical Guidelines.

Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered "YES". Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman's terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care.

Decoding Radiology Reports: Artificial Intelligence-Large Language Models Can Improve the Readability of Hand and Wrist Orthopedic Radiology Reports.

The purpose of this study was to assess the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of hand and wrist radiology reports. The radiology reports of 100 hand and/or wrist radiographs, 100 hand and/or wrist computed tomography (CT) scans, and 100 hand and/or wrist magnetic resonance imaging (MRI) scans were extracted. The following prompt command was inserted into the AI-LLM: "Explain this radiology report to a patient in layman's terms in the second person: [Report Text]." The report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for the original radiology report and the AI-LLM-generated report. The accuracy of the AI-LLM report was assessed via a 5-point Likert scale. Any "hallucination" produced by the AI-LLM-generated report was recorded. There was a statistically significant improvement in mean FRES scores and FKRL scores in the AI-LLM-generated radiograph report, CT report, and MRI report. For all AI-LLM-generated reports, the mean reading level improved to below an eighth-grade reading level. The mean Likert score for the AI-LLM-generated radiograph report, CT report, and MRI report was 4.1 ± 0.6, 3.9 ± 0.6, and 3.9 ± 0.7, respectively. The hallucination rate in the AI-LLM-generated radiograph report, CT report, and MRI report was 3%, 6%, and 6%, respectively. This study demonstrates that AI-LLM effectively improves the readability of hand and wrist radiology reports, underscoring the potential application of AI-LLM as a promising and innovative patient-centric strategy to improve patient comprehension of their imaging reports.Level of Evidence: IV.

Applications of Tissue Engineering

This article is written from a beginner's perspective to elucidate the concept of tissue engineering through its applications. It elaborates on the role of tissue engineering in burn skin treatment and drug delivery systems for cancer therapy. The first part of this article discusses the challenges and current issues related to burn injuries and the engineered techniques for their treatment. It highlights the need for scaffolds in skin tissue regeneration, along with the materials and methods used for treatment. The second part focuses on drug delivery mechanisms in tissue engineering, specifically in cancer treatments, explaining the carrier and release mechanisms of drugs targeting cancer cells. Most information was gathered from scientific articles, following the selection of focused applications. The basics of these applications were studied and explained in layman's terms, forming the foundation for an application-based learning experience. Personal reflections and comments are synthesized in the conclusion, making it accessible for those who want to learn about tissue engineering at a glance.

Fine-tuning large language models for rare disease concept normalization.

We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama 2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ∼20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLMs to identify named medical entities from clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

From technical to understandable: Artificial Intelligence Large Language Models improve the readability of knee radiology reports.

The purpose of this study was to evaluate the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of knee radiology reports. Reports of 100 knee X-rays, 100 knee computed tomography (CT) scans and 100 knee magnetic resonance imaging (MRI) scans were retrieved. The following prompt command was inserted into the AI-LLM: 'Explain this radiology report to a patient in layman's terms in the second person:[Report Text]'. The Flesch-Kincaid reading level (FKRL) score, Flesch reading ease (FRE) score and report length were calculated for the original radiology report and the AI-LLM generated report. Any 'hallucination' or inaccurate text produced by the AI-LLM-generated report was documented. Statistically significant improvements in mean FKRL scores in the AI-LLM generated X-ray report (12.7 ± 1.0-7.2 ± 0.6), CT report (13.4 ± 1.0-7.5 ± 0.5) and MRI report (13.5 ± 0.9-7.5 ± 0.6) were observed. Statistically significant improvements in mean FRE scores in the AI-LLM generated X-ray report (39.5 ± 7.5-76.8 ± 5.1), CT report (27.3 ± 5.9-73.1 ± 5.6) and MRI report (26.8 ± 6.4-73.4 ± 5.0) were observed. Superior FKRL scores and FRE scores were observed in the AI-LLM-generated X-ray report compared to the AI-LLM-generated CT report and MRI report, p < 0.001. The hallucination rates in the AI-LLM generated X-ray report, CT report and MRI report were 2%, 5% and 5%, respectively. This study highlights the promising use of AI-LLMs as an innovative, patient-centred strategy to improve the readability of knee radiology reports. The clinical relevance of this study is that an AI-LLM-generated knee radiology report may enhance patients' understanding of their imaging reports, potentially reducing the responder burden placed on the ordering physicians. However, due to the 'hallucinations' produced by the AI-LLM-generated report, the ordering physician must always engage in a collaborative discussion with the patient regarding both reports and the corresponding images. Level IV.

Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content.

The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance. In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience. We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis. We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building. The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.

Advances in liquefied natural gas processes

Over the past 30 years, a considerable world trade in LNG has developed. Today, LNG represents a significant component of the energy consumption of many countries and has been profitable to both the exporting host countries and their energy company partners. The attention of LNG producers have now been directed towards improved production. All latest plants have been sized around this number. Some of them have been designed by optimizing existing layout, other brand new and few required the optimization of centrifugal compressors and so the introduction of some novelty to maximize production given a certain driver. Improvements in the aerodynamic design have been necessary to maximize efficiency and increase operating range; advanced rotordynamic design to handle more capacity, new casings to increase design pressure and reduce the number are some of the innovations introduced to advance LNG operations. Novelties have not been limited to main refrigerant compressor but also to auxiliaries such as Boil Off Gas (BOG), CO2, End Flash. Eventually also new drivers have been qualified for LNG plant operations and other are under study for its high efficiency and possible future application. Extensive application of modular construction techniques will reduce the time and cost of construction in remote areas of the world. This article aims to explain, in layman terms, LNG basic knowledge, exploration, production and advancement. Throughout the article, references have been drawn from a wide range of resources and author’s personal industry experience. It is intended to use the article as a vehicle to share oil &amp; gas industry knowledge with a wide range of audience.

Online for On Call: A Study Assessing the Use of Internet Resources Including ChatGPT among On-Call Radiology Residents in India.

Background The information-seeking behavior of the radiology residents on call has undergone modernization in the recent times given the advent of easy to access, reliable online resources, and robust artificial intelligence chatbots such as Chat Generative Pre-Trained Transformer (ChatGPT). Purpose The aim of this study was to conduct a baseline analysis among the residents to understand the best way to meet information needs in the future, spread awareness about the existing resources, and narrow down to the most preferred online resource. Methods and Materials A prospective, descriptive study was performed using an online survey instrument and was conducted among radiology residents in India. They were questioned on their demographics, frequency of on call, fatigue experienced on call, and preferred information resources and reasons for choosing them. Results A total of 286 residents participated in the survey. All residents had used the Internet radiology resources during on-call duties. The most preferred resource material was Radiopaedia followed by Radiology Assistant. IMAIOS e-Anatomy was the most preferred anatomy resource. There was significant ( p < 0.05) difference in relation to the use of closed edit peer-reviewed literature among the two batches with it being used almost exclusively by third year residents. In the artificial intelligence-aided ChatGPT section, 61.8% had used the software at least once while being on call, of them 57.6% responded that the information was inaccurate, 67.2% responded that the information was insufficient to aid in diagnosis, 100% felt that the lack of images in the software made it an unlikely resource that would be used by them in the future, and 85.8% agreed that they would use it for providing reporting templates in the future. In the suggestions for upcoming versions, 100% responded that images should be included in the description provide by the chatbot, and 74.5% felt that references for the information being provided should be included as it reaffirms the reliability of the information. Conclusions Presently, we find that Radiopaedia met most of the requirements as an ideal online radiology resource according to the residents. In the present-day scenario, ChatGPT is not considered as an important on-call radiology education resource first because it lacks images which is quintessential for a budding radiologist, and second, it does not have any reference or proof for the information that it is providing. However, it may be of help to nonmedical professionals who need to understand radiology in layman's terms and to radiologists for patient report preparation and research writing.

Exploring undergraduate nursing students’ ineffective communication behaviors in simulation: A thematic analysis

Effective communication is essential to safe and competent nursing practice. Literature surrounding nursing students’ communicative patterns in high-fidelity simulation is limited and dated. There is a need to determine if new challenges in communication are present or if nurse educators are recognizing the same ineffective patterns over time. Qualitative thematic analysis using existing data from a junior-level nursing acute care simulation was completed. Data consisted of 22 clinical group transcripts representing 171 undergraduate nursing students. Themes identified include poor word choice, depersonalization, and intra/interprofessional communication breakdowns. Many of the findings were consistent with previous literature; however, a new finding of poor word choice was identified. This was identified in students’ inability to place patient education in appropriate and professional layman's terms. Ineffective communication can negatively affect patient safety and outcomes. Opportunities to develop therapeutic communication should be woven throughout the nursing curriculum through deliberative practice opportunities.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports

PurposeTo evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult. MethodsWe randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt “Explain this radiology report to a patient in layman's terms in second person: <Report Text>”. Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance. ResultsMean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DiscussionOur study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

A survey of Optimal Transport for Computer Graphics and Computer Vision

AbstractOptimal transport is a long‐standing theory that has been studied in depth from both theoretical and numerical point of views. Starting from the 50s this theory has also found a lot of applications in operational research. Over the last 30 years it has spread to computer vision and computer graphics and is now becoming hard to ignore. Still, its mathematical complexity can make it difficult to comprehend, and as such, computer vision and computer graphics researchers may find it hard to follow recent developments in their field related to optimal transport. This survey first briefly introduces the theory of optimal transport in layman's terms as well as most common numerical techniques to solve it. More importantly, it presents applications of these numerical techniques to solve various computer graphics and vision related problems. This involves applications ranging from image processing, geometry processing, rendering, fluid simulation, to computational optics, and many more. It is aimed at computer graphics researchers desiring to follow optimal transport research in their field as well as optimal transport researchers willing to find applications for their numerical algorithms.

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

Clean energy by proven new energy technology

Extended dependence on fossil fuels and other consumable resources to meet the energy requirements of the modern world and to provide the high living standards required by modern society; have been driving mankind toward the exhaustion of current fuel and energy resources. Hence, this article provides an infinite energy device that can be used to produce energy constantly by using buoyancy and gravitational force. The infinite energy device comprises a positive buoyant stable vessel body (the vessel is attached to an external tank with bars) having at its base (inside) an extendable/retractable chamber with the lock to open and close the intake valve for incoming water through the valve's hole from the lower end of the vessel. The water exits the chamber via a pipe due to the external pressure from a weight plate. The continuous flow of water from the pipe can be further used to generate energy. This device is not a completely isolated system from the rest of the universe; we do not know the far future so it is not a perpetual motion device and thus does not violate physics principles. I have a working prototype, and I have included as much information as possible about the device so that the experiment can be reproduced. Therefore, I included supporting photos. In layman's terms, this device has the advantage of a sequence of five systems. Lateral water displacement reduces the depth distance to send water above; buoyant force (75.40 kgf) acts as an upward force to expand a chamber of the device's vessel (inside) to store energy in the form of the gravitational potential energy of a weight plate (21.5 kgf). The buoyant force (75.40 kgf) is greater than gravitational force (21.5 kgf) due to which a steady state is reached with a net force equal to 53.9 kgf. Incoming water enters the chamber through an intake valve underneath. This is a float valve system. The valve is used to close by the buoyant force. It is initially locked by hand before automatic locking systems enclose the chamber. The hydraulic lift principle (system) is used to pressurize the chamber via the weight plate (gravity). Pascal's principle and gravity thus bring the chamber water outside the vessel and above the surrounding water level through a pipe. The (falling) water generates electricity in the water turbine generator and distributes electrical energy for the automatic locking of the intake valve and the rest, for our use. This is the main benefit of the device. The objective of the device is to bring the surrounding (chamber) water above water level by using buoyant force and gravity. And, I have demonstrated the prototype.

Fake-news-free evidence-based communication for proper vein-lymphatic disease management.

Published scientific evidence demonstrate the current spread of healthcare misinformation in the most popular social networks and unofficial communication channels. Up to 40% of the medical websites were identified reporting inappropriate information, moreover being shared more than 450,000 times in a 5-year-time frame. The phenomenon is particularly spread in infective diseases medicine, oncology and cardiovascular medicine. The present document is the result of a scientific and educational endeavor by a worldwide group of top experts who selected and analyzed the major issues and related evidence-based facts on vein and lymphatic management. A section of this work is entirely dedicated to the patients and therefore written in layman terms, with the aim of improving public vein-lymphatic awareness. The part dedicated to the medical professionals includes a revision of the current literature, summing up the statements that are fully evidence-based in venous and lymphatic disease management, and suggesting future lines of research to fulfill the still unmet needs. The document has been written following an intense digital interaction among dedicated working groups, leading to an institutional project presentation during the Universal Expo in Dubai, in the occasion of the v-WINter 2022 meeting.

Yolo Versions Architecture: Review

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing items in images.This article, will be focusing on comparing the main differences among the YOLO version's Architecture, and will discuss its evolution from YOLO to YOLOv8, its network architecture, newfeatures, and applications. Itstarts by looking at the basic ideas and design of the first YOLO model, which laid the groundwork for the following improvements in the YOLO family. In additionally, this article will provide a step-by-step guide on how to use the YOLO version architecture, Understanding the primary drivers, feature development, constraints, and even relationships for the versions is crucial as the YOLO versions advance.Researchers interested in object detection, especially beginning researchers, would find this paper useful and enlightening

Individualized Approaches to Diabetes Care in Nursing Homes

Although diabetes care has been widely established as a single entity, data in older, frail individuals with numerous comorbidities and have limited polymedication. This group accounts for a sizable number of nursing home residents (NHs). We came together as a group of experts with multidisciplinary (endocrinologists, geriatricians, Nurses staff, general practitioners and diabetologists) with real-world practice in this field, which is becoming more and more important globally, to compile practical, straightforward guidance on the managing of senior, fragile patients of diabetes. An Individual Care Plan (ICP), which is presented in layman's terms, is the cornerstone of a patient's diabetes treatment. This is due to the demands placed on personnel of NH (medical coordinator, manager, nurses, and caregiver working at the front lines, and the other caregiver worker which is undertraining). The actual purpose of this document, which is released when the patient is admitted, is to made prescriptions ensure that given at and followed after admission. It details the need for proper treatment, regular monitoring, and dates and times for essential examinations and testing. This entails keeping tabs on the patient's HbA1c, as well as their blood and urine glucose levels and any complications that may arise from the condition (hypoglycemia, cardiovascular disease, foot disorders, ocular problems, malnutrition, kidney failure and peripheral neuropathy). Consequentially, staff education on the unique challenges of care for an elderly person with diabetes, emergency procedures, and maintaining an up-to-date ICP for use by medical professionals is essential. Keywords: Diabetes, nursing home, polymedication

Usability of a gamified antibiotic resistance awareness mobile application: A qualitative evaluation

AbstractAntibiotic resistance has been one of the global health threats, yet the awareness of this issue remained low. Hence, an antibiotic resistance awareness app (Stop and End the Drastic Antibiotic Resistance [SEDAR]) was developed to improve public awareness of this issue. To identify the usability problems and improve the app's design before its deployment to the real user, a study on usability must be conducted. The purpose of this paper is to reveal the findings from the usability evaluation of the SEDAR app and present the necessary critical revisions made to improve the usability further. This study adopted guerrilla testing, observation, and retrospective probing methods to evaluate the usability of the SEDAR app. In total, eight participants were involved in this study. Based on the qualitative data, the SEDAR app has good usability. The participants found that it is easy to use, and the navigation within the app is straightforward. The information on antibiotics and antibiotic resistance conveyed through the app is easy to understand due to laymen's terms instead of using scientific terms. A substantial revision suggested by a few participants to further improve the SEDAR app was to add another language selection, the Malay language, to help local users with difficulties understanding English. The SEDAR app has acceptable usability. It is sufficient for users to learn more about antibiotics and antibiotic resistance as the app is easy to use and straightforward. The preliminary data suggested that SEDAR can potentially increase public awareness of antibiotics and antibiotic resistance.

Individualized Approaches to Diabetes Care in Nursing Homes

Although diabetes care has been widely established as a single entity, data in older, frail individuals with numerous comorbidities and have limited polymedication. This group accounts for a sizable number of nursing home residents (NHs). We came together as a group of experts with multidisciplinary (endocrinologists, geriatricians, Nurses staff, general practitioners and diabetologists) with real-world practice in this field, which is becoming more and more important globally, to compile practical, straightforward guidance on the managing of senior, fragile patients of diabetes. An Individual Care Plan (ICP), which is presented in layman's terms, is the cornerstone of a patient's diabetes treatment. This is due to the demands placed on personnel of NH (medical coordinator, manager, nurses, and caregiver working at the front lines, and the other caregiver worker which is under training). The actual purpose of this document, which is released when the patient is admitted, is to made prescriptions ensure that given at and followed after admission. It details the need for proper treatment, regular monitoring, and dates and times for essential examinations and testing. This entails keeping tabs on the patient's HbA1c, as well as their blood and urine glucose levels and any complications that may arise from the condition (hypoglycemia, cardiovascular disease, foot disorders, ocular problems, malnutrition, kidney failure and peripheral neuropathy). Consequentially, staff education on the unique challenges of care for an elderly person with diabetes, emergency procedures, and maintaining an up-to-date ICP for use by medical professionals is essential. Keywords: Diabetes care, nursing home, staff nurses