Medical Context Research Articles

Nonverbal behaviors perceived as most empathic in a simulated medical context

Perceiving empathy from healthcare professionals contributes to clinical benefits. Yet, for methodological and ethical reasons, the factors affecting perceived empathy, such as how the nonverbal behaviors of professionals interact, are less understood than those influencing the actual act of empathizing. Two online studies examined how the perception of empathy in a medical context of pain was influenced by factors related to digital healthcare professionals (DHPs) and participants acting as suffering patients. In Study 1 (n = 123), participants watched videos of DHPs showing variations in gaze direction, posture, and facial expression to rate perceived empathy from a visual patient perspective. They perceived more empathy from the face expressing pain, regardless of gaze, posture, and gender of the DHPs. The sex of participants also modulated perceived empathy. Study 2 (n = 116) expanded Study 1 by adding faces expressing pain and sadness of varying intensities, along with perspective-taking instructions, to determine whether higher perceived empathy for the face expressing pain stems from its congruence with the medical pain context. Participants perceived more empathy in faces expressing sadness than pain. Sadness and pain interacted differently with the effects of intensity, posture, and gaze direction. This work challenges the idea that exact congruence with the patient's affective state is necessary and further contributes to investigating nonverbal behaviors of empathy.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Better Living Through Creepy Technology? Exploring Tensions Between a Novel Class of Well-Being Apps and Affective Discomfort in App Culture

Well-being apps promise to improve people's lives. Yet evidence shows that the data-hungry app culture that contextualizes well-being apps normalizes the user experience of affective discomfort. This apparent contradiction raises a difficult question: Is it responsible to ask people to improve their well-being by engaging further with an app culture that normalizes affective discomfort? We approached this question by deploying an online, scenario-based survey (n=688) about a fictional, but realistic well-being app called "Thalia." Thalia represents a novel class of well-being apps that are envisioned to: (i) utilize AI-driven facial recognition and analysis; and (ii) collect data for use in medical contexts. We found that people perceived Thalia to be affectively discomfiting even as they judged Thalia to be beneficial. Such findings imply a trade-off between 'better living through technology' and the negative affective implications of surveillance capitalistic app culture. Such a trade-off necessitates high-level analysis of just what "well-being" means in the context of contemporary app culture. Through analysis and discussion, we explore a troubling interplay between novel well-being apps and affective discomfort that requires careful attention from HCI researchers if human-centered well-being -- flourishing -- is truly what our products are intended to foster.

Geriatrics and Ageing in the Soviet Union: Medical, Political and Social Contexts, edited by Susan Grant and Isaac McKean Scarborough

Geriatrics and Ageing in the Soviet Union: Medical, Political and Social Contexts, edited by Susan Grant and Isaac McKean Scarborough

Google Gemini and Bard artificial intelligence chatbot performance in ophthalmology knowledge assessment.

With the popularization of ChatGPT (Open AI, San Francisco, California, United States) in recent months, understanding the potential of artificial intelligence (AI) chatbots in a medical context is important. Our study aims to evaluate Google Gemini and Bard's (Google, Mountain View, California, United States) knowledge in ophthalmology. In this study, we evaluated Google Gemini and Bard's performance on EyeQuiz, a platform containing ophthalmology board certification examination practice questions, when used from the United States (US). Accuracy, response length, response time, and provision of explanations were evaluated. Subspecialty-specific performance was noted. A secondary analysis was conducted using Bard from Vietnam, and Gemini from Vietnam, Brazil, and the Netherlands. Overall, Google Gemini and Bard both had accuracies of 71% across 150 text-based multiple-choice questions. The secondary analysis revealed an accuracy of 67% using Bard from Vietnam, with 32 questions (21%) answered differently than when using Bard from the US. Moreover, the Vietnam version of Gemini achieved an accuracy of 74%, with 23 (15%) answered differently than the US version of Gemini. While the Brazil (68%) and Netherlands (65%) versions of Gemini performed slightly worse than the US version, differences in performance across the various country-specific versions of Bard and Gemini were not statistically significant. Google Gemini and Bard had an acceptable performance in responding to ophthalmology board examination practice questions. Subtle variability was noted in the performance of the chatbots across different countries. The chatbots also tended to provide a confident explanation even when providing an incorrect answer.

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

SummaryComputer assisted medical diagnosis technology is widely used in the field of medical assistance to assist doctors in making diagnostic decisions. But as the number of patients increases, the diagnostic pressure on doctors gradually increases, and more efficient computer‐aided medical diagnosis technology is needed to improve the accuracy of doctors' diagnosis. Today's computer‐based medically assisted diagnostic technologies suffer from the inability to fully simulate physical attributes and environmental factors, the large computational resources required for high‐precision models, the need for professional training for user operation, and the limited intuition for innovative design. For improving diagnostic efficiency, this study designs a medical Question answering intelligent interaction system in view of artificial intelligence algorithms. The system is constructed with an active interactive intelligent Q&A model consisting of a medical reasoning module and a medical examination recommendation module. Then, it uses local Bayesian network algorithm as the foundation to establish an intelligent strategy optimization network. And it puts forward the answer selection model of medical Question answering in view of hierarchical interaction for natural language processing tasks in the medical context. The performance test results show that when the diagnostic end threshold of the medical reasoning module is 0.8, the shortest diagnostic path is 3.33. When the diagnostic threshold is 0.85, the maximum length of the diagnostic path is 4.66, and the maximum difference between the diagnostic paths is 1.33, which is basically not affected by the diagnostic end threshold. The local Bayesian network algorithm can reduce the impact of noise features and extract more valuable information. The accuracy of the multilevel interactive answer selection model on the Stanford Natural Language Inference dataset without using external resources reached 89.2%. The ablation test results show that the overall accuracy of the model is 89.64%. The visualization results of the attention weight distribution test between interaction layers show that under different levels of interaction, the attention distribution will undergo significant changes.

The generic structure potential (GSP) of STI doctor-patient interactions in southwest Nigeria

This study describes the generic structure potential (GSP) of sexually transmitted infection (STI) consultations in Nigeria. It is a further effort from previous studies not only in its consideration of the phase structure of consultations in a specific medical context, but also in its exploration of both the broad and narrow generic structure potential of the consultations. Fifty audio recordings of mixed-visit doctor-patient interactions in hospitals in three states in southwest Nigeria constitute the data. The data were analysed using Halliday and Hasan’s (1985) Generic Structure Potential theory (GSP). The analysis revealed four broad stages in the structural organisation of STI medical interactions, namely Opening (O), Diagnostic Interactions (DI), Treatment Recommendations (TR), and Closing (C). The obligatory elements in the broad catalogue are the diagnostic interactions and treatment recommendations, while the narrow GSP of the phases revealed the doctor’s problem elicitation as the obligatory element in the opening phase, the patient’s problem presentation activity was solely obligatory in DI, and the doctor’s treatment procedure, the only mandatory activity of the TP phase. The study concludes that the diagnostic interaction and treatment recommendation phases define medical consultations in STI (and HIV) encounters in Nigeria.

Parental concerns about oral health of children: Is ChatGPT helpful in finding appropriate answers?

Artificial intelligence (AI) is becoming an important part of our lives owing to increased data availability and improved power of computing. One of the recently launched modalities of AI, ChatGPT, is being enormously used worldwide for different types of tasks. In medical context, its use is being explored for clinical queries, academia, research help, etc. Further, literature suggests that parents seek information about health of their children using different Internet resources and would surely turn toward ChatGPT for the same, as this chatbot model is easy to use, generates "one" response, and is available without any subscription. ChatGPT generates a response using text cues and applying different algorithms on prepublished literature but is still in its naïve state; hence, it is imperative to validate the generated responses. Accordingly, we planned this study to determine the clarity, correctness, and completeness of some Frequently asked questions (FAQs) about child's oral health, from a mother's perspective. The study design was a vignette-based survey and included a set of 23 questions, for which ChatGPT was interviewed from the perspective of an imaginary parent. The answers responded by ChatGPT were copied "verbatim," and a Google survey form was designed. The survey form was validated and then sent to 15 pediatric dentists, and the responses were mainly collected on the Likert's scale with a provision of one open-ended question aiming to determine "what they would have added" to this generated response as an expert in the field. The responses on Likert's scale were condensed and values ≥4 were considered 'adequate and acceptable' while scores ≤3, were considered 'inadequate'. The generated responses and comments mentioned by different respondents in the open-ended question were critiqued in reference to the existing literature. Overall, the responses were found to be complete and logical and in clear language, with only some inadequacies being reported in few of the answers.

Assessment of Serum Cathepsin k and Lipid Profile in Chronic Coronary Syndrome Patients

Abstract Background: The most affected public illness around the world in both industrialized and unindustrialized countries is chronic coronary syndrome (CCS). In a medical context, the measurement of lipid profile in the blood is considered as one of the most common diagnostic techniques. In addition, there is a correlation between increased level of cathepsin k (CatK) and CCS, and thus cathepsin is considered a useful biomarker for CCS. Objective: For the assessment of CatK, triglyceride (TG), cholesterol, low density lipoprotein (LDL), very-low-density lipoprotein (VLDL), and high density lipoprotein (HDL) level and to examine the probable relation of them with CCS in Babylon province. Materials and Methods: CatK, TG, cholesterol, LDL, VLDL, and HDL were estimated in 100 subjects; 50 patients with CCS and 50 healthy subjects participated in this study. Patients and control groups with an age ranged above 40 years. The CatK level was assessed by sandwich-ELISA technique whereas the level of TG, cholesterol, and HDL was assessed in serum by enzymatic colorimetric method. Also, cholesterol-LDL was measured by using Friedewald equation. Results: Serum level of CatK displayed a significant increase in CCS patients (P ≤ 0.01) compared with control group, whereas serum cholesterol-HDL level significantly decreased (P = 0.001). Also serum levels of cholesterol and cholesterol-LDL a significant increase (P ≤ 0.001), (P = 0.00) compared with control group. In contrast, the current study observed non-significant change in serum TG and VLDL (P = 0.45), (P = 0.71) respectively in CCS patients. Conclusion: Circulating CatK is a good biomarker for CCS disorders and that higher levels of CatK and lipid profile are closely related to the presence of CCS among CCS patients.

Profile of adverse drug reactions reported via the Continuum+ platform: Results from three-year regional follow-up

In 2017, the Continuum+ platform was launched to provide a monitoring solution to home-based cancer care patients: AKO@dom monitoring. This platform also offers the follow-up of adverse drug reactions (ADRs) via direct notification to regional centers of pharmacovigilance (RCPVs). According to previous studies, the AKO@dom monitoring has successfully maintained treatment at the maximum effective dosage, managing ADRs and patient satisfaction. However, on the pharmacovigilance side, opinions are more divided. Due to the launch of the AKO@dom-PICTO experimentation in December 2021, in which our RCPV takes part, and to provide more data on pharmacovigilance, we decided to conduct a descriptive analysis of cases reported to our RCPV via the Continuum+ platform between 2019 and 2022. During these three years, we analyzed 1070 events, corresponding to 37patients. Patients were primarily women (74.8%) aged around seventy with breast cancer. The most used drugs were tyrosine kinase inhibitors: palbociclib (29.7%), axitinib (16.2%), and cabozantinib (13.2%). Patients had an average of 8 ADRs, including one serious and/or unexpected ADR. Although the Continuum+ platform makes it possible to considerably limit under-reporting in pharmacovigilance, it has shortcomings. The lack of medical elements and context in notifications is a massive problem for analyzing pharmacovigilance reports. Improved access to the platform's medical information for RCPVs and pharmacovigilance training for healthcare professionals would make Continuum+ a helpful tool in pharmacovigilance.

Generative models improve fairness of medical classifiers under distribution shifts

Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and ‘labeling’ by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.

Therapeutic potential of inositol to PCOS: An overview of administration, efficacy, and potential applications

Inositol, a group of naturally occurring sugar alcohols, has gained attention for the diverse physiological roles and therapeutic potential. This comprehensive overview examines the administration, efficacy, and potential applications of inositol in various medical contexts. Myo-inositol and D-chiro inositol are the two types of inositol covered in this review, exploring their mechanisms of action and biochemical pathways. Discussions on the administration methods, including supplements and dietary sources, provide insights into optimizing therapeutic outcomes. The review also explores the available data on the effectiveness of inositol in treating diseases like mental health disorders, metabolic disorders, and polycystic ovarian syndrome (PCOS). Furthermore, potential reproductive medicine, neurology, and other areas of applications are explored. This overview provides a comprehensive snapshot of the evolving landscape of inositol research, emphasizing its promising role in various therapeutic interventions and prompting further exploration of its multifaceted benefits.

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

A Comprehensive Approach to User Delegation and Anonymity within Decentralized Identifiers for IoT.

Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.

Regarding the implementation of foreign psychotherapeutic approaches in the mental health protection system of the population of Ukraine in conditions of war

Background. The work is devoted to the problem of preserving the mental health of the population of Ukraine. The emergence of stress-related mental disorders, including post-traumatic stress disorder, leads to a negative effect on families and communities, problems of mutual understanding, support and recovery. Against this background, there is no doubt about the need to implement the experience of Western colleagues in the mental health care system at all levels of care provision. The purpose of the work is to update the issue of the implementation of foreign psychotherapeutic approaches in the system of mental health care of the population of Ukraine in the conditions of war. Materials and methods. On the basis of the analysis of literary sources and information resources, a modern overview of the problem of introducing modern psychotherapeutic directions into the system of mental health protection of the population in wartime conditions was obtained. Discussion. An analytical review of pathogenetic prerequisites for the implementation of the experience of Western doctors in rehabilitation measures and psychological support measures was carried out. Implementation of strategies aimed at preventing mental illness through education, stress reduction, and development of psychosocial support can help reduce the prevalence of risk factors for the development of mental health problems. Justification of the implementation of Western experience is important for improving the provision of assistance in the mental health system in Ukraine, which will meet modern standards and take into account the peculiarities of Ukrainian culture and society.At the same time, ensuring effective rehabilitation for victims is an act of recognition of their dignity and rights to a full life. Conclusions. At the current stage, Western health care systems emphasize the prevention of mental illnesses through education, stress reduction, and the development of psychosocial support. Implementing these strategies can help reduce the prevalence of risk factors for developing mental health problems. The ability to integrate mental health care into the general medical context is a priority, as it can improve detection of mental illness, provide a comprehensive approach to treatment, and reduce stigmatisation in the society. Focusing on the scientific basis is a priority, because the proven base of Western approaches to the issue of maintaining mental health is based on scientific research and effective methods.

USER EXPERIENCE (UX) DESIGN IN MEDICAL PRODUCTS: THEORETICAL FOUNDATIONS AND DEVELOPMENT BEST PRACTICES

User Experience (UX) Design in Medical Products is pivotal in enhancing patient outcomes, satisfaction, and adherence to treatment regimens. This abstract delves into the theoretical underpinnings and best practices that underscore effective UX design within the medical domain. Grounded in human-centered design principles, this approach emphasizes understanding user needs, preferences, and limitations to develop inclusive, accessible, and intuitive interfaces. Integrating insights from behavioral psychology, UX designers leverage techniques such as persuasive design to promote healthy behaviors and facilitate adherence to medical protocols. Moreover, compliance with regulatory standards is paramount, necessitating a nuanced understanding of medical regulations and standards to ensure product safety and efficacy. Usability testing emerges as a crucial step, facilitating iterative improvements based on user feedback and ensuring that medical products are intuitive, error-resistant, and conducive to safe user interactions. Beyond functionality, considerations of data privacy and security are paramount, requiring UX designers to implement robust measures to safeguard patient confidentiality while maintaining seamless user experiences. Communication between patients and healthcare providers is also a focal point, with UX strategies aimed at enhancing information exchange, fostering trust, and facilitating informed decision-making. Additionally, optimizing workflow efficiency through streamlined task sequences and leveraging emerging technologies such as artificial intelligence and augmented reality further enhances the user experience in medical contexts. Through continual iteration and refinement, UX design in medical products strives to deliver compassionate, user-centric solutions that meet the diverse needs of patients and healthcare professionals alike, ultimately driving improved healthcare delivery and patient outcomes.
 Keywords: User Experience Design, Medical Products, Human-Centered Design, Behavioral Psychology, Regulatory Compliance, Usability Testing.

Open-Ended Coaxial Probe for Effective Reconstruction of Biopsy-Excised Tissues' Dielectric Properties.

Dielectric characterization is extremely promising in medical contexts because it offers insights into the electromagnetic properties of biological tissues for the diagnosis of tumor diseases. This study introduces a promising approach to improve accuracy in the dielectric characterization of millimeter-sized biopsies based on the use of a customized electromagnetic characterization system by adopting a coated open-ended coaxial probe. Our approach aims to accelerate biopsy analysis without sample manipulation. Through comprehensive numerical simulations and experiments, we evaluated the effectiveness of a metal-coating system in comparison to a dielectric coating with the aim for replicating a real scenario: the use of a needle biopsy core with the tissue inside. The numerical analyses highlighted a substantial improvement in the reconstruction of the dielectric properties, particularly in managing the electric field distribution and mitigating fringing field effects. Experimental validation using bovine liver samples revealed highly accurate measurements, particularly in the real part of the permittivity, showing errors lower than 1% compared to the existing literature data. These results represent a significant advancement for the dielectric characterization of biopsy specimens in a rapid, precise, and non-invasive manner. This study underscores the robustness and reliability of our innovative approach, demonstrating the convergence of numerical analyses and empirical validation.

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making.

Multimodal learning for temporal relation extraction in clinical texts.

This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, particularly through the analysis of extensive reports and notes concerning patient experiences. Our approach involves the development of a bimodal architecture that seamlessly integrates information from both text documents and knowledge graphs. This integration serves to infuse common knowledge about events into the temporal relation extraction process. Rigorous testing was conducted on diverse clinical datasets, emulating real-world scenarios where the extraction of temporal relationships is paramount. The performance of our proposed bimodal architecture was thoroughly evaluated across multiple clinical datasets. Comparative analyses demonstrated its superiority over existing methods reliant solely on textual information for temporal relation extraction. Notably, the model showcased its effectiveness even in scenarios where not provided with additional information. The amalgamation of textual data and knowledge graph information in our bimodal architecture signifies a notable advancement in the field of temporal relation extraction. This approach addresses the critical need for a more profound understanding of narrative processes in medical contexts. In conclusion, our study introduces a pioneering bimodal architecture that harnesses the synergy of text and knowledge graph data, exhibiting superior performance in temporal relation extraction from medical documents. This advancement holds significant promise for improving the comprehension of patients' healthcare journeys and enhancing the overall effectiveness of extracting temporal relationships in complex medical narratives.

Antioxidant Effect of a Combination of S-Acetyl-L-Glutathione, Vitamin E, Silybum Marianumon Hepatic Cells under Oxidative Stress: An In Vitro Study

Oxidative stress plays a key role in the pathogenesis of liver diseases and can be involved in the inflammatory process of liver cells. The aim of this vitro study is to assess the antioxidant efficacy of three distinct components (fermented S-Acetyl -L-glutathione, Silybum marianum (L.) Gaern. and vitamin E all-rac-alpha-tocopheryl acetate) both individually and in combination (Glutasyl product). In addition, we also evaluated the combined antioxidant effect of the three ingredients on human hepatic cells subjected to oxidative stress induced by H2O2 treatment. The results showed the dose-dependent antioxidant potential of the three components suggesting promising applications in medical contexts. The synergistic antioxidant effects observed for the Glutasyl was more than the individual components. Additionally, Glutasyl showed not-toxic activity on human hepatocytes and it could be considered useful in mitigating cytotoxicity induced by oxidative stress.