Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era.

In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.

Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.

Background: Addressing disparities in living kidney donation requires making information accessible across literacy levels, especially important given that the average American adult reads at an 8th-grade level. This study evaluated the effectiveness of ChatGPT, an advanced AI language model, in simplifying living kidney donation information to an 8th-grade reading level or below. Methods: We used ChatGPT versions 3.5 and 4.0 to modify 27 questions and answers from Donate Life America, a key resource on living kidney donation. We measured the readability of both original and modified texts using the Flesch-Kincaid formula. A paired t-test was conducted to assess changes in readability levels, and a statistical comparison between the two ChatGPT versions was performed. Results: Originally, the FAQs had an average reading level of 9.6 ± 1.9. Post-modification, ChatGPT 3.5 achieved an average readability level of 7.72 ± 1.85, while ChatGPT 4.0 reached 4.30 ± 1.71, both with a p-value <0.001 indicating significant reduction. ChatGPT 3.5 made 59.26% of answers readable below 8th-grade level, whereas ChatGPT 4.0 did so for 96.30% of the texts. The grade level range for modified answers was 3.4-11.3 for ChatGPT 3.5 and 1-8.1 for ChatGPT 4.0. Conclusion: Both ChatGPT 3.5 and 4.0 effectively lowered the readability grade levels of complex medical information, with ChatGPT 4.0 being more effective. This suggests ChatGPT's potential role in promoting diversity and equity in living kidney donation, indicating scope for further refinement in making medical information more accessible.

AI-driven translations for kidney transplant equity in Hispanic populations.

Health equity and accessing Spanish kidney transplant information continues being a substantial challenge facing the Hispanic community. This study evaluated ChatGPT's capabilities in translating 54 English kidney transplant frequently asked questions (FAQs) into Spanish using two versions of the AI model, GPT-3.5 and GPT-4.0. The FAQs included 19 from Organ Procurement and Transplantation Network (OPTN), 15 from National Health Service (NHS), and 20 from National Kidney Foundation (NKF). Two native Spanish-speaking nephrologists, both of whom are of Mexican heritage, scored the translations for linguistic accuracy and cultural sensitivity tailored to Hispanics using a 1-5 rubric. The inter-rater reliability of the evaluators, measured by Cohen's Kappa, was 0.85. Overall linguistic accuracy was 4.89 ± 0.31 for GPT-3.5 versus 4.94 ± 0.23 for GPT-4.0 (non-significant p = 0.23). Both versions scored 4.96 ± 0.19 in cultural sensitivity (p = 1.00). By source, GPT-3.5 linguistic accuracy was 4.84 ± 0.37 (OPTN), 4.93 ± 0.26 (NHS), 4.90 ± 0.31 (NKF). GPT-4.0 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 4.95 ± 0.22 (NKF). For cultural sensitivity, GPT-3.5 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 5.00 ± 0.00 (NKF), while GPT-4.0 scored 5.00 ± 0.00 (OPTN), 5.00 ± 0.00 (NHS), 4.90 ± 0.31 (NKF). These high linguistic and cultural sensitivity scores demonstrate Chat GPT effectively translated the English FAQs into Spanish across systems. The findings suggest Chat GPT's potential to promote health equity by improving Spanish access to essential kidney transplant information. Additional research should evaluate its medical translation capabilities across diverse contexts/languages. These English-to-Spanish translations may increase access to vital transplant information for underserved Spanish-speaking Hispanic patients.

Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.

The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations-outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.

Associations of salivary aldosterone levels during pregnancy with maternal blood pressure and birth weight-for-gestational age in a Mexico City birth cohort.

OBJECTIVE: To determine associations of maternal salivary aldosterone with blood pressure (BP) in pregnancy and infant birth weight-for-gestational age (BWGA). METHODS: We measured maternal salivary aldosterone, BP and BWGA z-scores in 471 Mexico City pregnancy cohort participants and performed multivariable linear regression of BP and BWGA on log-aldosterone levels. RESULTS: Log-aldosterone was positively associated with diastolic BP (ß = 0.12 95% CI: 0.04, 0.21). There were no main effects of log-aldosterone on BWGA. However, we detected an interaction between log-aldosterone and BP in association with BWGA; higher log-aldosterone was associated with lower BWGA in the lowest (ß = -0.12, 95% CI: -0.26, 0.02) and highest (ß = -0.12, 95% CI: -0.29, 0.06) BP tertiles. In contrast, in the middle BP tertile the association was positive (ß = 0.09, 95% CI: -0.02, 0.20), p for interaction = 0.03. CONCLUSION: Higher maternal salivary aldosterone is positively associated with diastolic BP and may affect fetal growth differently depending on concurrent maternal blood pressure.

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention.

Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.

Prevalencia de obesidad y factores de riesgo asociados en adultos mexicanos: resultados de la Ensanut 2022.

OBJETIVO: Describir la prevalencia de obesidad en adultos, medida a través del índice de masa corporal (IMC) y la circunferencia de cintura (CC), estratificando por factores de riesgo y comorbilidades. Material y métodos. Se analizó la información de 8 563 participantes en la Encuesta Nacional de Salud y Nutrición 2022 (Ensanut 2022). Se clasificó la obesidad por IMC y por CC. Se calcularon razones de momios (RM) para asociar la obesidad con factores de riesgo y diagnóstico de comorbilidades. RESULTADOS: La prevalencia de sobrepeso fue 38.3%, obesidad 36.9% y obesidad abdominal (OA) 81.0%. Las mujeres tuvieron una mayor RM (1.4) de tener obesidad y OA (2.5). Los adultos con obesidad tenían una mayor posibilidad de tener diagnóstico de diabetes (RM 1.7), hipertensión (3.6) y dislipidemia (RM 2.3) que los adultos con IMC normal. CONCLUSIONES: La prevalencia de obesidad en adultos mexicanos es una de las más altas a nivel mundial y está asociada con los factores de riesgo y enfermedades crónicas más frecuentes. Se requieren políticas públicas multisectoriales para prevenir y controlar la obesidad.

Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.

BACKGROUND: Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results. METHODS: Using the OPTN/UNOS 2017-2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results. RESULTS: Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison. CONCLUSIONS: Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.

Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT.

The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT's performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT's potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.

AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat.

Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

Performance of ChatGPT on Nephrology Test Questions.

BACKGROUND: ChatGPT is a novel tool that allows people to engage in conversations with an advanced machine learning model. ChatGPT's performance in the US Medical Licensing Examination is comparable with a successful candidate's performance. However, its performance in the nephrology field remains undetermined. This study assessed ChatGPT's capabilities in answering nephrology test questions. METHODS: Questions sourced from Nephrology Self-Assessment Program and Kidney Self-Assessment Program were used, each with multiple-choice single-answer questions. Questions containing visual elements were excluded. Each question bank was run twice using GPT-3.5 and GPT-4. Total accuracy rate, defined as the percentage of correct answers obtained by ChatGPT in either the first or second run, and the total concordance, defined as the percentage of identical answers provided by ChatGPT during both runs, regardless of their correctness, were used to assess its performance. RESULTS: A comprehensive assessment was conducted on a set of 975 questions, comprising 508 questions from Nephrology Self-Assessment Program and 467 from Kidney Self-Assessment Program. GPT-3.5 resulted in a total accuracy rate of 51%. Notably, the employment of Nephrology Self-Assessment Program yielded a higher accuracy rate compared with Kidney Self-Assessment Program (58% versus 44%; P < 0.001). The total concordance rate across all questions was 78%, with correct answers exhibiting a higher concordance rate (84%) compared with incorrect answers (73%) ( P < 0.001). When examining various nephrology subfields, the total accuracy rates were relatively lower in electrolyte and acid-base disorder, glomerular disease, and kidney-related bone and stone disorders. The total accuracy rate of GPT-4's response was 74%, higher than GPT-3.5 ( P < 0.001) but remained below the passing threshold and average scores of nephrology examinees (77%). CONCLUSIONS: ChatGPT exhibited limitations regarding accuracy and repeatability when addressing nephrology-related questions. Variations in performance were evident across various subfields.

Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications.

Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel artificial intelligence (AI) large language model (LLM) renowned for its exceptional ability to generate human-like responses across various tasks. However, whether ChatGPT can effectively assist medical professionals in identifying relevant literature is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in identifying references to literature reviews in nephrology. We keyed the prompt "Please provide the references in Vancouver style and their links in recent literature on… name of the topic" into ChatGPT-3.5 (03/23 Version). We selected all the results provided by ChatGPT and assessed them for existence, relevance, and author/link correctness. We recorded each resource's citations, authors, title, journal name, publication year, digital object identifier (DOI), and link. The relevance and correctness of each resource were verified by searching on Google Scholar. Of the total 610 references in the nephrology literature, only 378 (62%) of the references provided by ChatGPT existed, while 31% were fabricated, and 7% of citations were incomplete references. Notably, only 122 (20%) of references were authentic. Additionally, 256 (68%) of the links in the references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of the references. Moreover, among those with a link provided, the link was correct in only 20% of cases, and 3% of the references were irrelevant. Notably, an analysis of specific topics in electrolyte, hemodialysis, and kidney stones found that >60% of the references were inaccurate or misleading, with less reliable authorship and links provided by ChatGPT. Based on our findings, the use of ChatGPT as a sole resource for identifying references to literature reviews in nephrology is not recommended. Future studies could explore ways to improve AI language models' performance in identifying relevant nephrology literature.

Enhancing Kidney Transplant Care through the Integration of Chatbot.

Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.

Ethical Implications of Chatbot Utilization in Nephrology.

This comprehensive critical review critically examines the ethical implications associated with integrating chatbots into nephrology, aiming to identify concerns, propose policies, and offer potential solutions. Acknowledging the transformative potential of chatbots in healthcare, responsible implementation guided by ethical considerations is of the utmost importance. The review underscores the significance of establishing robust guidelines for data collection, storage, and sharing to safeguard privacy and ensure data security. Future research should prioritize defining appropriate levels of data access, exploring anonymization techniques, and implementing encryption methods. Transparent data usage practices and obtaining informed consent are fundamental ethical considerations. Effective security measures, including encryption technologies and secure data transmission protocols, are indispensable for maintaining the confidentiality and integrity of patient data. To address potential biases and discrimination, the review suggests regular algorithm reviews, diversity strategies, and ongoing monitoring. Enhancing the clarity of chatbot capabilities, developing user-friendly interfaces, and establishing explicit consent procedures are essential for informed consent. Striking a balance between automation and human intervention is vital to preserve the doctor-patient relationship. Cultural sensitivity and multilingual support should be considered through chatbot training. To ensure ethical chatbot utilization in nephrology, it is imperative to prioritize the development of comprehensive ethical frameworks encompassing data handling, security, bias mitigation, informed consent, and collaboration. Continuous research and innovation in this field are crucial for maximizing the potential of chatbot technology and ultimately improving patient outcomes.

Perceptions of food environments in the school and at home during Covid-19: An online cross-sectional study of parents, teachers and experts from Latin America.

BACKGROUND: The high prevalence of overweight and obesity in children from Latin America (LA) have been related to obesogenic food environments. Besides, the negative effects of the Covid-19 pandemic should also be considered. The objective of this research was to describe and compare the perceptions of parents, teachers, and experts in LA of food environments at home and school that favor healthy habits in schoolchildren in pre Covid-19 stage and during the pandemic. METHODS: This study used a survey self-reporting regarding conditions at home and the school favoring healthy habits, for three profiles: parents, primary school teachers, and experts. A fisher exact test was used to establish the difference between the response categories between countries and profiles. Logistic regression models were used to determine the probability of response in the levels of importance adjusted for sex and nationality. RESULTS: Information from 954 questionnaires was reported: 48.4% experts, 32.0% teachers, and 19.6% parents. There were differences in the perception of food environments at school between profiles (p<0.001). In multivariate logistic regression models, experts and teachers were 20% more likely to give greater importance to elements of the food environment at school compared to parents (p<0.001). CONCLUSIONS: Our findings showed that parents were less likely to perceive important elements of the school food environment compared to experts and teachers. Interventions are required to improve healthy eating environments that consider children's interpersonal mediators.

Temporal differences in the myoelectric activity of lower limb muscles during rearfoot and forefoot running: A statistical parametric mapping approach.

Forefoot (FF) and rearfoot (RF) running techniques can induce different lower-limb muscle activation patterns. However, few studies have evaluated temporal changes in the electromyographic activity (EMG) of lower limb muscles during running. The aim of this study was to compare temporal changes in EMG amplitude between RF and FF running techniques. Eleven recreational runners ran on a treadmill at a self-selected speed, once using a RF strike pattern and once using a FF strike pattern (randomized order). The EMG of five lower limb muscles [rectus femoris (RFe), biceps femoris (BF), tibialis anterior (TA), medial and lateral gastrocnemius (MG and LG)] was evaluated, using bipolar electrodes. EMG data from the RF and FF running techniques was then processed and compared with statistical parametric mapping (SPM), dividing the analysis of the running cycle into stance and swing phases. The MG and LG muscles showed higher activation during FF running at the beginning of the stance phase and at the end of the swing phase. During the end of the swing phase, the TA muscle's EMG amplitude was higher, when the RF running technique was used. A higher level of co-activation between the gastrocnemius and TA muscles was observed in both stance and swing phases using RF. The myoelectric behaviour of the RFe and BF muscles was similar during both running techniques. The current findings highlight that the two running techniques predominately reflect adjustments of the shank and not the thigh muscles, in both phases of the running cycle.HighlightsStatistical parametric mapping (SPM) can reveal temporal differences in muscle activity between running techniques.The medial and lateral gastrocnemius muscles were more active at specific time-instants of the initial stance and late swing phases during forefoot (FF) running compared to rearfoot (RF) running.Higher activation was observed for the tibialis anterior muscle at the end of the swing phase during RF runningContrary to the muscle activity differences observed in the leg muscles, the muscle activity of the thigh muscles was similar during RF and FF running.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review.

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

Electromyographic and kinematic parameters of the shoulder in wheelchair rugby players: case reports.

Wheelchair rugby was created as part of the rehabilitation for patients with spinal cord injury. The biomechanical analysis of wheelchair propulsion (WP) in these athletes seems to be a key element to understand the reasons behind musculoskeletal injuries. This case reports study aimed to describe the electromyographic activity and kinematic parameters of the shoulder during the propulsion phases on the wheelchair in two Paralympic rugby players (A1 and A2) with spinal cord injury. Myoelectric activity (three portions of the deltoid, biceps and triceps brachii) and kinematics of the shoulder were assessed during the push (PP) and recovery (RP) phases. These variables were calculated considering ten propulsion cycles by each athlete. The results showed a different muscle activation between players, A1 described a high average amplitude of the anterior deltoid (PP = 58.44 ± 16.35%MVC; RP = 43.16 ± 13.48%MVC) in both propulsion phases, while A2 generated high average activity of triceps brachii (29.28 ± 10.63%MVC) and middle deltoid (46.53 ± 14.48%MVC), during PP and RP, respectively. At the same time, the player with a C7-T1 spinal cord injury (A2) showed a higher range of motion in the three plans, considering both propulsion phases.

Location In Vivo of the Innervation Zone in the Human Medial Gastrocnemius Using Imposed Contractions: A Comparison of the Usefulness of the M-Wave and H-Reflex.

The anatomical territory where the neuromuscular junctions are grouped corresponds to the innervation zone (IZ). This can be located in-vivo using high-density electromyography and voluntary muscle contractions. However, in patients with motor impairment, the use of contractions imposed by electrical stimulation (ES) could be an alternative. The present study has two aims: Firstly, to describe the location of the IZ in-vivo of the medial gastrocnemius (MG) using imposed contractions by ES. Secondly, to compare the usefulness of M-waves and H-reflexes to localize the IZs. Twenty-four volunteers participated (age: 21.2 ± 1.5 years). ES was elicited in the tibial nerve to obtain M-waves and H-reflexes in the MG. The evaluators used these responses to localize the IZs relative to anatomical landmarks. M-wave and H-reflex IZ frequency identification were compared. The IZs of the MG were mostly located in the cephalocaudal direction, at 39.7% of the leg length and 34% of the knee's condylar width. The IZs were most frequently identified in the M-wave (83.33%, 22/24) compared to the H-reflex (8.33%, 2/24) (p > 0.001). Imposed contractions revealed that the IZ of the MG is located at 39.7% of the leg length. To locate the IZs of the MG muscle, the M-wave is more useful than the H-reflex.

Prevalencias de estado de nutrición en recién nacidos mexicanos por peso y longitud al nacimiento: un análisis de los certificados de nacimiento del Sinac.

Objective: To describe the national by federal entity prevalence of the nutritional status of weight and length at birth. Materials and Methods: Cross-sectional descrip­tive study. Data from 1 907 341 alive newborns in 2017, registered in the Subsistema de Información sobre Nacimientos (Sinac), were analyzed. The percentiles for weight and length were estimated in the INTERGROWTH-21st platform. Results: The prevalence of small gestational age (SGA) and insufficient length (IL) was 7.4 and 4.8%, respectively. Differ­ences in the prevalence of IL, SGA and large for the gestational age (LGA) by sex were recorded (p <0.01). The entities with the highest prevalence of SGA were Estado de México and Yucatán (10.4%); Sonora (15.3%) and Baja California Sur (16.8%) of LGA. Conclusion: Sizing the nutritional status at birth allows the identification of entities that require targeted actions to reduce the risks associated with malnutrition.

Objetivo. Describir la prevalencia nacional por entidad federativa del estado de nutrición de peso y longitud al nacimiento. Material y métodos. Estudio transversal descriptivo. Se analizaron datos de 1 907 341 recién nacidos vivos en 2017, registrados en el Subsistema de Información sobre Nacimientos (Sinac). Los percentiles para peso y lon­gitud se estimaron en la plataforma INTERGROWTH-21st. Resultados. La prevalencia de pequeños para la edad ges­tacional (PEG) y longitud insuficiente (LI) fue de 7.4 y 4.8%, respectivamente. Se registraron diferencias por sexo en las prevalencias de LI, PEG y grandes para la edad gestacional (GEG) (p <0.01). Las entidades con mayores prevalencias de PEG (10.4%) fueron Estado de México y Yucatán. De GEG, fueron Sonora (16.8%) y Baja California Sur (15.3%). Conclu­sión. Dimensionar el estado de nutrición al nacer permite identificar entidades que requieren acciones focalizadas para disminuir los riesgos asociados con la malnutrición.