The patients' perspective of bedside teaching in the post-Covid era

BACKGROUND

Bedside teaching with real patients is vital to the education of medical students. With the onset of the Covid-19 pandemic in 2020, all student-patient interactions were suspended in order to prevent the spread of the virus. Research has previously focussed on the medical professional and medical student views concerning bedside teaching, with less of a focus on patient insights.

AIMS

The purpose and aims of this study were to explore the patient's perspectives of encountering medical students and contributing to their learning in the clinical setting, beyond the Covid restrictions.

METHODS

Qualitative semi-structured interviews were carried out with a cohort of Mayo University hospital inpatients, who had participated in a bedside teaching session. An interview guide was employed to facilitate consistent and analogous data. An interpretative phenomenological approach was used for data analysis yielding a number of themes.

RESULTS

Participants recognised the value of their involvement in the clinical education of future doctors, in addition to the positive impacts of such contributions for their own future healthcare. Participants had no concerns about partaking in bedside teaching sessions relating to Covid infection.

CONCLUSIONS

This study assessed the opinions of hospital inpatients with regards to participating in clinical teaching sessions and found that they do not perceive their role as passive.

Scoping Review: A Hybrid Delivery Model of Pain Management

OBJECTIVES

This scoping review explores hybrid healthcare models combining telehealth and in-person visits for pain management. It examines their components, effectiveness compared to traditional care, advantages, and disadvantages of telehealth, and the influence of future technologies.

DESIGN

The review followed the JBI scoping review methodology and used the PRISMA-ScR checklist. Studies on hybrid pain management models involving adult patients and clinicians were included.

DATA SOURCES

Searches were conducted in PubMed, CINAHL, and Google Scholar, along with gray literature from healthcare organizations.

REVIEW/ANALYSIS METHODS

Studies were screened based on Population, Concept, and Context (PCC) criteria. Data extraction followed a modified JBI tool, with thematic analysis and descriptive statistical summaries of study findings.

RESULTS

Hybrid care models combining telehealth and in-person visits improve access to pain management, particularly in rural areas, and reduce costs. Telehealth provides convenience but faces barriers such as privacy concerns, technological disparities, and patient preference for in-person visits. In-person care remains crucial for diagnostics and patient-provider trust. Future technologies like machine learning show promise for enhancing these models.

CONCLUSIONS

Hybrid models offer a flexible, cost-effective approach to chronic pain management. While telehealth expands access, challenges include privacy issues and digital inequities. Future technologies will likely improve these models' personalization and efficiency.

NURSING PRACTICE IMPLICATIONS

Nurses should be trained in telehealth technologies to facilitate the implementation of hybrid models while continuing to provide essential in-person care to engage patients and build trust.

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review

The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling these complex connections. However, effectively utilizing this data often requires strong and efficient learning algorithms, especially when dealing with limited labeled data. Self-supervised learning (SSL) has emerged as a powerful paradigm for leveraging unlabeled data to learn effective representations. This paper presents a comprehensive review of SSL approaches specifically designed for graph-structured data in healthcare applications. We explore the challenges and opportunities associated with healthcare data and assess the effectiveness of SSL techniques in real-world healthcare applications. Our discussion encompasses various healthcare settings, such as disease prediction, medical image analysis, and drug discovery. We critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. To the best of our knowledge, this is the first comprehensive review of SSL applied to graph data in healthcare, providing valuable guidance for researchers and practitioners looking to leverage these techniques to enhance outcomes and drive progress in the field.

Complementary Repair Therapy as a Trending Topic in Discogenic Pain: A Bibliometric Study Over the Past 40 Years

Background and Aims

Bibliometric analysis has been frequently employed for visualizing trends within a particular scientific domain. The pain associated with discogenic origins has a significant impact on one's quality of life, but there is currently a lack of bibliometric analysis in the literature. Hence, this study aimed to examine related research in the field and identify the latest topics that are currently trending by conducting a bibliometric analysis.

Methods

The Bibliometrix which developed in the statistical R-packages was used for the data analysis. All related eligible publications were identified, and studies published from 1982 to 2023 were extracted from the Web of Science database.

Results

Disc repair, bone marrow cells, platelet-rich plasma, and the activation of inflammatory responses were identified as the trending topics after analyzing 977 journal articles. The most productive and influential journal was SPINE (Phila Pa 1976), which accounted for the largest publications and highest H index. The most productive and locally cited authors were Takahashi K., Ohtori S., and Aoki Y. from Chiba University. The top three productive and globally cited institutions were Chiba University, followed by University of California San Francisco and Korea University. The USA, China, and Japan were demonstrated as the most productive and globally cited countries.

Conclusion

This study performed the first bibliometric analysis on discogenic pain and provided valuable insights into the latest trending topics in the field. Analysis reveals that recent research has primarily focused on complementary and regenerative approaches for repairing painful discs, as well as the role of inflammatory responses in disc pathology.

Electroacupuncture improves postoperative cognitive dysfunction by inhibiting ferroptosis via the TFR1-DMT1-FPN pathway

OBJECTIVE

The aim of this study was to investigate the role of ferroptosis in the occurrence of postoperative cognitive dysfunction (POCD) using a mouse model and to elucidate whether electroacupuncture (EA) can improve POCD by suppressing ferroptosis via the transferrin receptor 1 (TFR1)-divalent metal transporter 1 (DMT1)-ferroportin (FPN) pathway.

METHODS

The experiment involved three groups: the control group, the POCD group and the POCD + EA group. The POCD animal model was established using sevoflurane anesthesia and tibial fracture. Cognitive and behavioral changes in mice were assessed using the novel object recognition test (NORT) and the Morris water maze (MWM) test, 1 and 3 days after surgery. Transmission electron microscopy was performed to observe changes in the mitochondrial structure of hippocampal tissue. Enzyme-linked immunosorbent assay was conducted to determine the levels of glutathione (GSH) and iron ion (Fe) concentrations. Western blot analysis was used to measure the expression of TFR1, DMT1 and iron pump protein. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was employed to detect the mRNA levels of DMT1 and FPN.

RESULTS

Based on the experimental results of the MWM test and the NORT, we found that EA can improve POCD in mice. Observation by projection electron microscopy showed that EA improved the mitochondrial structure in the hippocampus. The enzyme-linked immunosorbent assay (ELISA) results showed that EA suppressed ferroptosis in the hippocampal area. The qRT-PCR and Western blot results suggested that EA suppresses ferroptosis by regulating the TFR1-DMT1-FPN pathway.

CONCLUSION

This study reveals that sevoflurane and tibial fractures cause cognitive damage through the mechanism of ferroptosis, while EA may inhibit ferroptosis through the TFR1-DMT1-FPN pathway and improve POCD when induced in this way.

Nursing Students' Perspectives on Integrating Artificial Intelligence Into Clinical Practice and Training: A Qualitative Descriptive Study

Background

The integration of artificial intelligence (AI) into healthcare has introduced transformative tools to enhance clinical decision-making and streamline workflows. In nursing, a profession characterized by human-centric care, AI adoption offers both significant opportunities and notable challenges. However, the perspectives of nursing students, future professionals, on integrating AI into clinical practice and education remain underexplored.

Aim

This study aimed to explore nursing students' perceptions of incorporating AI into their clinical training and professional practice, with a focus on identifying benefits, challenges, and potential areas for improvement.

Methods

A qualitative descriptive design explored the experiences and attitudes of 25 nursing students from five colleges in Dhaka, Bangladesh. Participants were purposively sampled to ensure diverse educational and clinical backgrounds. Semi-structured interviews in Bangla, lasting 40-50 min, were audio-recorded, transcribed, and translated into English. Data were collected from May 8, 2024 to August 10, 2024. Data were analyzed using thematic analysis to identify patterns and themes. Credibility was ensured through member checking, dependability via an audit trail, and confirmability through peer debriefing. Data visualization tools were used to map thematic relationships effectively.

Results

Thematic analysis revealed four major themes: (1) AI integration in nursing education, (2) ethical and professional concerns, (3) preparedness for AI-driven practice, and (4) AI's impact on nursing practice. Participants expressed both optimism about AI's potential to improve accuracy and efficiency and apprehension about their readiness to use AI effectively in practice.

Conclusion

The findings underscore the need for comprehensive curriculum reforms that incorporate AI training, address ethical concerns, and emphasize the role of AI as a supportive tool rather than a replacement for human expertise. These insights provide a roadmap for integrating AI into nursing education while preserving the compassionate core of nursing practice.

Standardization of criteria in MacCAT-T and MacCAT-CR for monoclonal anti-beta-amyloid antibodies: A Delphi study

Introduction

Assessing capacity to consent to treatment and participation in clinical research with monoclonal anti-beta-amyloid antibodies is critical, especially given the frequent uncertainty in the eligible population. Capacity tends to be underestimated in Alzheimer's patients and overestimated in those with mild cognitive impairment (MCI).

Methods

Using the Delphi method, an international expert panel (N = 21) was surveyed in two waves.

Results

The participants reached consensus on 85 % of identified features, 90 % of benefits, and 88 % of risks.

Discussion

The resulting standard emphasizes the understanding subscale of the MacArthur competence assessment tools (MacCAT) for both treatment and research, supporting use across clinical and research settings. Despite proven utility, only 4 % of psychiatrists currently use tools like MacArthur Competence Assessment Tool for Treatment (MacCAT-T). This consensus aims to promote wider adoption of capacity assessments, integrating them routinely into clinical practice to balance patient autonomy with beneficence.

AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer

AIM

Recent randomized controlled trials and meta-analyses have demonstrated a reduction in the anastomotic leak rate when indocyanine green fluorescence angiography (ICGFA) is used versus when it is not in colorectal resections. We have previously demonstrated that an artificial intelligence (AI) model, AUGUR-AI, can digitally represent in real time where experienced ICGFA users would place their surgical stapler based on their interpretation of the fluorescence imagery. The aim of this study, called AUGUR-AIM, is to validate this method across multiple clinical sites with regard to generalizability, usability and accuracy while generating new algorithms for testing and determining the optimal mode of deployment for the software device.

METHOD

This is a prospective, observational, multicentre European study involving patients undergoing resectional colorectal surgery with ICGFA as part of their standard clinical care enrolled over a 1-year period. Video recordings of the ICGFA imagery will be computationally analysed both in real time and post hoc by AUGUR-AI, with the operating surgeon blinded to the results, testing developed algorithms iteratively versus the actual surgeon's ICGFA interpretation. AI-based interpretation of the fluorescence signal will be compared with the actual transection site selected by the operating surgeon and usability optimized.

CONCLUSION

AUGUR-AIM will validate the use of AUGUR-AI to interpret ICGFA imagery in real time to the level of an expert ICGFA user, building on our previous work to include a larger, more diverse patient and surgeon population. This could allow future progression to develop the AI model into a usable clinical tool that could provide decision support, including to new/infrequent ICGFA users, and documentary support of the decision made by experienced users.

Oncogenic KRASG12D Transfer from Platelet-like Particles Enhances Proliferation and Survival in Non-Small Cell Lung Cancer Cells

In the tumor context, platelets play a significant role in primary tumor progression, dissemination and metastasis. Analysis of this interaction in various cancers, such as non-small cell lung cancer (NSCLC), demonstrate that platelets can both transfer and receive biomolecules (e.g. RNA and proteins) to and from the tumor at different stages, becoming tumor-educated platelets. To investigate how platelets are able to transfer oncogenic material, we developed in vitro platelet-like particles (PLPs), from a differentiated MEG-01 cell line, that stably carry RNA and protein of the KRASG12D oncogene in fusion with GFP. We co-cultured these PLPs with NSCLC H1975 tumor cells to assess their ability to transfer this material. We observed that the generated platelets were capable of stably expressing the oncogene and transferring both its RNA and protein forms to tumor cells using qPCR and imaging techniques. Additionally, we found that coculturing PLPs loaded with GFP-KRASG12D with tumor cells increased their proliferative capacity at specific PLP concentrations. In conclusion, our study successfully engineered an MEG-01 cell line to produce PLPs carrying oncogenic GFP-KRASG12D simulating the tumor microenvironment, demonstrating the efficient transfer of this oncogene to tumor cells and its significant impact on enhancing proliferation.

[Management of neuromuscular block during general anesthesia : Results of a survey in Germany in 2020 compared to the recommendations of the first European guidelines in 2023]

Since the last surveys on the utilization of muscle relaxants in general anesthesia in 2000 and 2005, it can be assumed that the introduction of sugammadex for the rapid and safe reversal of neuromuscular blocks as well as the establishment of new surgical techniques, have had an impact on the use of muscle relaxants in general anesthesia. A web-based questionnaire was used to survey anesthesia departments and outpatient surgery centers regarding the use of neuromuscular blocking agents. The aim was a comparison with previous surveys and the statistical analysis was descriptive. The response rate of the 1027 anesthesia departments contacted was 16.5%. The availability of neuromuscular monitoring in hospitals continues to improve. The most frequently used drug for rapid sequence induction is now rocuronium rather than succinylcholine. Sugammadex is now available as a drug in 86% of the responding anesthesia departments. The concept of complete relaxation for laparoscopic surgery called deep block is used by 60% of those surveyed. The results are analyzed and discussed in the context of the first European guidelines published in 2023 by the European Society of Anaesthesiology and Intensive Care (ESAIC). They show that with respect to the use of succinylcholine and the support by new surgical techniques have changed the management of neuromuscular block since the last surveys. The use of neuromuscular monitoring to control the management of muscle relaxation is still not standard in all hospitals, so that an additional patient risk cannot be ruled out.

Evaluating the Efficacy of Perplexity Scores in Distinguishing AI-Generated and Human-Written Abstracts

RATIONALE AND OBJECTIVES

We aimed to evaluate the efficacy of perplexity scores in distinguishing between human-written and AI-generated radiology abstracts and to assess the relative performance of available AI detection tools in detecting AI-generated content.

METHODS

Academic articles were curated from PubMed using the keywords "neuroimaging" and "angiography." Filters included English-language, open-access articles with abstracts without subheadings, published before 2021, and within Chatbot processing word limits. The first 50 qualifying articles were selected, and their full texts were used to create AI-generated abstracts. Perplexity scores, which estimate sentence predictability, were calculated for both AI-generated and human-written abstracts. The performance of three AI tools in discriminating human-written from AI-generated abstracts was assessed.

RESULTS

The selected 50 articles consist of 22 review articles (44%), 12 case or technical reports (24%), 15 research articles (30%), and one editorial (2%). The perplexity scores for human-written abstracts (median; 35.9 IQR; 25.11-51.8) were higher than those for AI-generated abstracts (median; 21.2 IQR; 16.87-28.38), (p=0.057) with an AUC=0.7794. One AI tool performed less than chance in identifying human-written from AI-generated abstracts with an accuracy of 36% (p>0.05) while another tool yielded an accuracy of 95% with an AUC=0.8688.

CONCLUSION

This study underscores the potential of perplexity scores in detecting AI-generated and potentially fraudulent abstracts. However, more research is needed to further explore these findings and their implications for the use of AI in academic writing. Future studies could also investigate other metrics or methods for distinguishing between human-written and AI-generated texts.

The Role of ChatGPT in Personal Statements for Plastic Surgery Residency Applications: Program Directors' Perspective

Background

Personal statements are a required component of plastic surgery residency applications but can be extremely time- and labor-intensive. Artificial intelligence (AI) programs like ChatGPT can streamline personal statement writing, but their use, especially if undisclosed, can have ethical implications. This study elucidates the perspective of plastic surgery residency program directors (PDs) regarding the importance of personal statements in reviewing applicants and whether ChatGPT should be utilized.

Methods

An anonymous, 6-question multiple-choice survey was designed and administered in 3 rounds via REDCap to 120 current plastic surgery residency PDs. An additional email reminder was administered by the principal investigator. Data was collected and reported in aggregate.

Results

The survey response rate was 28.6%. Most PDs (73.5%) reported that personal statements were somewhat important in determining interviewees and the rank list; 85.3% of PDs were not confident in their ability to determine if ChatGPT was utilized. Additionally, 85.3% of residencies reported not utilizing AI-detection software, although 11.8% plan to implement one. Only 8.8% of PDs believed ChatGPT use to be ethically appropriate in all aspects of personal statement creation, whereas others believed it was only appropriate for brainstorming (11.8%), editing (14.7%), or writing (5.9%). Finally, 58.8% of PDs believed ChatGPT use to be unethical in all parts of personal statement creation.

Conclusions

The utilization of AI could have a profound impact on streamlining personal statement creation, but its use has many ethical implications. Currently, the majority of surveyed PDs feel the use of ChatGPT to be unethical in any form during personal statement writing.

Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies

Natural Language Processing (NLP) has the potential to revolutionise clinical research utilising Electronic Health Records (EHR) through the automated analysis of unstructured free text. Despite this potential, relatively few applications have entered real-world clinical practice. This paper aims to introduce the whole pipeline of NLP methodologies for EHR analysis to the clinical researcher, with case studies to demonstrate the application of these methods in the existing literature. Essential pre-processing steps are introduced, followed by the two major classes of analytical frameworks: statistical methods and Artificial Neural Networks (ANNs). Case studies which apply statistical and ANN-based methods are then provided and discussed, illustrating information extraction tasks for objective and subjective information, and classification/prediction tasks using supervised and unsupervised approaches. State-of-the-art large language models and future directions for research are then discussed. This educational article aims to bridge the gap between the clinical researcher and the NLP expert, providing clinicians with a background understanding of the NLP techniques relevant to EHR analysis, allowing engagement with this rapidly evolving area of research, which is likely to have a major impact on clinical practice in coming years.

A Comprehensive Review of the Antioxidant, Antimicrobial, and Therapeutic Efficacies of Black Cumin (Nigella sativa L.) Seed Oil and Its Thymoquinone

Black cumin (Nigella sativa L.) (family Ranunculaceae) is a largely utilized therapeutic herb worldwide. This comprehensive review discusses the pharmacological benefits of black cumin seed oil, focusing on its bioactive component thymoquinone (TQ). The review is structured as follows: First, we examine the antimicrobial properties of black cumin oil, followed by an analysis of its antioxidant capabilities. Finally, we explore its therapeutic potential, particularly in neurodegenerative diseases and COVID-19. Phytochemicals from N. sativa have exhibited potential for developing novel preventive and therapeutic strategies against jaundice, gastrointestinal disorders, skin diseases, anorexia, conjunctivitis, dyspepsia, intrinsic hemorrhage, amenorrhea, paralysis, anorexia, rheumatism, diabetes, hypertension, fever, influenza, eczema, asthma, cough, bronchitis, and headache. The broader spectrum of application for N. sativa and its essential bioactives have certainly enhanced the commercial value of this seed oil. TQ, a major constituent of black cumin seed oil, has numerous beneficial properties. Researchers have extensively studied black cumin seed oil and its major component, TQ. These studies have revealed a wide range of pharmacological properties, including anticancer, immunomodulatory, analgesic, antimicrobial, antidiabetic, and anti-inflammatory effects. Additionally, TQ has shown neuroprotective, spasmolytic, bronchodilatory, hepatoprotective, renoprotective, gastroprotective, and antioxidant activities.

Evaluating ChatGPT as a patient resource for frequently asked questions about lung cancer surgery-a pilot study

OBJECTIVE

Chat-based artificial intelligence programs like ChatGPT are reimagining how patients seek information. This study aims to evaluate the quality and accuracy of ChatGPT-generated answers to common patient questions about lung cancer surgery.

METHODS

A 30-question survey of patient questions about lung cancer surgery was posed to ChatGPT in July 2023. The ChatGPT-generated responses were presented to 9 thoracic surgeons at 4 academic institutions who rated the quality of the answer on a 5-point Likert scale. They also evaluated if the response contained any inaccuracies and were prompted to submit free text comments. Responses were analyzed in aggregate.

RESULTS

For ChatGPT-generated answers, the average quality ranged from 3.1 to 4.2 of 5.0, indicating they were generally "good" or "very good." No answer received a unanimous 1-star (poor quality) or 5-star (excellent quality) score. Minor inaccuracies were found by at least 1 surgeon in 100% of the answers, and major inaccuracies were found in 36.6%. Regarding ChatGPT, 66.7% of surgeons thought it was an accurate source of information for patients. However, only 55.6% thought they were comparable with answers given by experienced thoracic surgeons, and only 44.4% would recommend it to their patients. Common criticisms of ChatGPT-generated answers included lengthiness, lack of specificity regarding surgical care, and lack of references.

CONCLUSIONS

Chat-based artificial intelligence programs have potential to become a useful information tool for patients with lung cancer surgery. However, the quality and accuracy of ChatGPT-generated answers need improvement before thoracic surgeons would consider this method as a primary education source for patients.

Progress of KRAS G12C inhibitors in the treatment of refractory colorectal cancer and strategies for drug resistance response

Colorectal cancer is the third most prevalent cancer in the world. Early screening and detection of tumours, active surgical radical treatment, postoperative adjuvant chemotherapy, targeted therapy, and immunotherapy are performed based on pathological staging and immunohistochemistry. Even with these measures, the 5-year survival rate of colorectal cancer is only 65%, and a considerable number of patients still experience tumour recurrence or even metastasis. The KRAS G12C mutation accounts for 3 to 4% of refractory colorectal cancer (advanced or metastatic colorectal cancer), and it was once believed that KRAS did not have a drug target until the emergence of KRAS G12C inhibitors provided targeted treatment for KRAS-mutated colorectal cancer. However, KRAS G12C inhibitors only produce moderate efficacy, and resistance occurs after a short remission. The mechanism of drug resistance in tumour cells is complex and diverse, and existing research has limited understanding of it. This review aims to elucidate the clinical trial progress of KRAS G12C inhibitors in refractory colorectal cancer, the research progress of drug resistance mechanisms, and the combined treatment strategies for drug resistance.

Comparing ChatGPT 3.5 and 4.0 in Low Back Pain Patient Education: Addressing Strengths, Limitations, and Psychosocial Challenges

BACKGROUND

Artificial intelligence tools like ChatGPT have gained attention for their potential to support patient education by providing accessible, evidence-based information. This study compares the performance of ChatGPT 3.5 and ChatGPT 4.0 in answering common patient questions about low back pain, focusing on response quality, readability, and adherence to clinical guidelines, while also addressing the models' limitations in managing psychosocial concerns.

METHODS

Thirty frequently asked patient questions about low back pain were categorized into 4 groups: Diagnosis, Treatment, Psychosocial Factors, and Management Approaches. Responses generated by ChatGPT 3.5 and 4.0 were evaluated on 3 key metrics: 1) response quality: rated on a scale of 1 (excellent) to 4 (unsatisfactory); 2) DISCERN criteria: evaluating reliability and adherence to clinical guidelines, with scores ranging from 1 (low reliability) to 5 (high reliability; and 3) readability: assessed using 7 readability formulas, including Flesch-Kincaid and Gunning Fog Index.

RESULTS

ChatGPT 4.0 significantly outperformed ChatGPT 3.5 in response quality across all categories, with a mean score of 1.03 compared to 2.07 for ChatGPT 3.5 (P < 0.001). ChatGPT 4.0 also demonstrated higher DISCERN scores (4.93 vs. 4.00, P < 0.001). However, both versions struggled with psychosocial factor questions, where responses were rated lower than for Diagnosis, Treatment, and Management questions (P = 0.04).

CONCLUSIONS

ChatGPT 3.5 and 4.0 limitations in addressing psychosocial concerns highlight the need for clinician oversight, particularly for emotionally sensitive issues. Enhancing artificial intelligence's capability in managing psychosocial aspects of patient care should be a priority in future iterations.

Large language models in critical care

The advent of chat generative pre-trained transformer (ChatGPT) and large language models (LLMs) has revolutionized natural language processing (NLP). These models possess unprecedented capabilities in understanding and generating human-like language. This breakthrough holds significant promise for critical care medicine, where unstructured data and complex clinical information are abundant. Key applications of LLMs in this field include administrative support through automated documentation and patient chart summarization; clinical decision support by assisting in diagnostics and treatment planning; personalized communication to enhance patient and family understanding; and improving data quality by extracting insights from unstructured clinical notes. Despite these opportunities, challenges such as the risk of generating inaccurate or biased information "hallucinations", ethical considerations, and the need for clinician artificial intelligence (AI) literacy must be addressed. Integrating LLMs with traditional machine learning models - an approach known as Hybrid AI - combines the strengths of both technologies while mitigating their limitations. Careful implementation, regulatory compliance, and ongoing validation are essential to ensure that LLMs enhance patient care rather than hinder it. LLMs have the potential to transform critical care practices, but integrating them requires caution. Responsible use and thorough clinician training are crucial to fully realize their benefits.

Pain Signaling by GPCRs and RTKs

Chronic pain is common and debilitating, yet is inadequately treated by current therapies, which can have life-threatening side effects. Treatments targeting G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs), key pain mediators, often fail in clinical trials for unknown reasons. Here, we discuss the recent evidence that GPCRs and RTKs generate sustained signals from multiprotein signaling complexes or signalosomes in intracellular compartments to control chronic pain. We evaluate the evidence that selective antagonism of these intracellular signals provides more efficacious and long-lasting pain relief than antagonism of receptors at the surface of cells. We highlight how the identification of coreceptors and molecular scaffolds that underpin pain signaling by multiple receptors has identified new therapeutic targets for chronic pain, surmounting the redundancy of the pain signaling pathway.

Impact of ChatGPT on Diabetes Mellitus Self-Management Among Patients in Saudi Arabia

BACKGROUND

Diabetes mellitus (DM) is a chronic condition requiring continuous self-management to prevent complications. Artificial intelligence (AI)-driven tools like ChatGPT (OpenAI, Inc., San Francisco, CA, USA) offer potential support in education, monitoring, and decision-making. However, research on its effectiveness in DM self-management remains limited, particularly in Saudi Arabia, necessitating further investigation into its role and impact.

PURPOSE

This study aims to analyze the impact of ChatGPT on DM self-management.

METHODS

A qualitative experimental design was adopted in this study. DM patients after interacting with ChatGPT for a week participated in the interviews, where their perceptions on its impact were recorded. A total of 25 DM patients participated in the study, whose results were analyzed using thematic analysis.

RESULTS

The analysis of interview data revealed 11 themes related to the impact of ChatGPT on DM self-management, which included informational support, personalized recommendations, motivation and support, assistance in decision-making, offering self-care reminders, facilitating communication with healthcare providers, facilitating peer support, providing mental health support, tracking and monitoring, conducting health assessments, and education and awareness.

CONCLUSION

ChatGPT has a positive impact on DM self-management. However, further research is needed due to ChatGPT's novel nature for generalizing results and extending its applicability to other areas of healthcare.

Shared medical decision making

Involving the patient in medical decision-making is called shared medical decision-making (SMD). While the concept of SMD is nothing new, implementation has been slow to develop within current clinical practice, although there is growing interest in this topic in the scientific literature. SMD requires full agreement with the patient, who becomes an actor in their own care, and whose goals sometimes differ from those of the doctor. In a systematic review, it was reported that 75% of surgeons were in favor of SMD, while only 54% of patients favored it. The tools that support SMD can be extremely variable; they are not merely a document of information but must offer guidance to help the patients clarify their choices. They must allow for quality time for discussion, even though the time spent on SMD is perceived as a hindrance to its widespread adoption. The objectives of this work are to specify the essential steps in setting up SMD, and the assessment tools and applications for SMD in digestive surgery.

Development of a YOLOv3-Based Model for Automated Detection of Thoracic Ossification of the Posterior Longitudinal Ligament and the Ligamentum Flavum on Plain Radiographs

Background/Objectives: This study aims to develop and validate a YOLOv3-based deep learning model for detecting ossification of the posterior longitudinal ligament (OPLL) and ossification of the ligamentum flavum (OLF) on lateral thoracic radiographs, improving early diagnosis and screening accessibility. Methods: A retrospective dataset of 356 lateral thoracic radiographs, including 176 with OPLL or OLF and 180 controls, was annotated by spine surgeons. The YOLOv3 model was trained using data augmentation and evaluated via five-fold cross-validation, with accuracy, precision, recall, and F1-score compared to two spine surgeons. Results: The model achieved 80.6% accuracy, 70.3% precision, 92.6% recall, and 79.9% F1-score, surpassing spine surgeons in accuracy and recall, especially for combined OPLL and OLF cases. Detection accuracy was 81.1% for OPLL, 53.3% for OLF, and 86.3% for combined cases. Conclusions: The YOLOv3-based model provides high accuracy and robust detection of OPLL and OLF on plain radiographs, offering an efficient and accessible screening tool.

Contrast Agents in Breast MRI: State of the Art and Future Perspectives

Contrast-enhanced magnetic resonance imaging (CE-MRI) has become an essential modality in breast cancer diagnosis and management. It is particularly used for locoregional staging, high-risk screening, monitoring treatment response, and assessing complications related to breast implants. The integration of gadolinium-based contrast agents (GBCAs) enhances the sensitivity and specificity of CE-MRI by providing detailed morphological and functional insights, particularly highlighting tumor neoangiogenesis. Despite its advantages, CE-MRI faces challenges such as high costs, limited accessibility, and concerns about gadolinium retention in tissues, prompting ongoing research into safer, high-relaxivity contrast agents like gadopiclenol. Advances in multiparametric imaging, including dynamic contrast-enhanced sequences and diffusion-weighted imaging, have refined diagnostic accuracy, enabling precise staging, and treatment planning. The introduction of abbreviated breast MRI (AB-MRI) protocols offers a promising solution to barriers of cost and scan duration, maintaining diagnostic efficacy while improving patient accessibility and comfort. Future innovations in contrast agents, imaging protocols, and patient-centered approaches hold the potential to further enhance the utility of breast MRI, ensuring equitable and effective application in global healthcare systems.

Predicting explainable dementia types with LLM-aided feature engineering

MOTIVATION

The integration of Machine Learning and Artificial Intelligence (AI) into healthcare has immense potential due to the rapidly growing volume of clinical data. However, existing AI models, particularly Large Language Models (LLMs) like GPT-4, face significant challenges in terms of explainability and reliability, particularly in high-stakes domains like healthcare.

RESULTS

This paper proposes a novel LLM-aided feature engineering approach that enhances interpretability by extracting clinically relevant features from the Oxford Textbook of Medicine. By converting clinical notes into concept vector representations and employing a linear classifier, our method achieved an accuracy of 0.72, outperforming a traditional n-gram Logistic Regression baseline (0.64) and the GPT-4 baseline (0.48), while focusing on high-level clinical features. We also explore using Text Embeddings to reduce the overall time and cost of our approach by 97%.

AVAILABILITY AND IMPLEMENTATION

All code relevant to this paper is available at: https://github.com/AdityaKashyap423/Dementia_LLM_Feature_Engineering/tree/main.

Small bowel obstruction outcomes according to compliance with the World Society of Emergency Surgery Bologna guidelines

BACKGROUND

Small bowel obstruction (SBO) is a common surgical emergency associated with substantial morbidity, hospital length of stay (LOS), and healthcare cost. The World Society of Emergency Surgery (WSES) Bologna guidelines provide evidence-informed recommendations for managing adhesive SBO, promoting timely surgical intervention (or non-operative management (NOM) when ischaemia, strangulation, or peritonitis are absent). However, guideline adoption and its impact on outcomes remain under studied. Compliance with the Bologna guidelines was evaluated to determine the impact of compliance on outcomes.

METHODS

SnapSBO, a prospective, multicentre, time-bound, observational cohort study, captured data on patients with adhesive SBO across diverse healthcare settings and patient populations. Patient care was categorized into: successful NOM, surgery after an unsuccessful appropriate trial of NOM (NOM-T), and direct to surgery (DTS). Compliance with diagnostic, therapeutic, and postoperative Bologna guideline recommendations was assessed as either complete or partial. Primary outcomes included adherence to the Bologna guidelines, LOS, complications, and the incidence of the composite metric 'optimal outcomes' (LOS ≤5 days, discharge without complications, and no readmission within 30 days).

RESULTS

Among 982 patients with adhesive SBO, successful NOM occurred in 561 (57.1%), 224 (22.8%) underwent NOM-T, and 197 (20.1%) proceeded DTS. The mean(s.d.) LOS was 5.3(9.0), 12.9(11.4), and 7.7(8.0) days respectively (P < 0.001). Optimal outcomes were achieved in 61.0%, 16.1%, and 37.6% respectively (P < 0.001) and full guideline compliance was observed in 17.2%, 10.1%, and 0.4% respectively.

CONCLUSION

Patients with adhesive SBO whose care was aligned with the Bologna guidelines had a shorter LOS and a greater incidence of optimal outcomes. Addressing evidence-to-practice gaps through implementation strategies that consider contextual factors will enhance guideline adoption and patient outcomes.

Innovative Approaches for the Treatment of Spinal Disorders: A Comprehensive Review

This comprehensive review explores the latest advancements in the management of spinal disorders, including minimally invasive surgical techniques, treatment of complex deformities, disc replacement technologies, and non-surgical approaches. The review highlights the potential of innovations such as robotic-assisted surgeries, regenerative medicine, and artificial intelligence to enhance precision, reduce recovery times, and improve patient outcomes. It also discusses the integration of wearable technologies and personalized medicine in tailoring treatments. Challenges such as high costs, accessibility issues, and limited long-term data are critically analyzed, alongside gaps in research, including a lack of diversity in study populations and insufficient economic evaluations. Future directions emphasize the need for multidisciplinary collaboration to develop durable, accessible, and personalized solutions to address the global burden of spinal disorders.

EGCG's anticancer potential unveiled: triggering apoptosis in lung cancer cell lines through in vitro investigation

Background

Novel treatment techniques are needed since lung cancer is still a major worldwide health concern. Green tea contains a component called epigallocatechin-3-gallate (EGCG), which has demonstrated potential anticancer properties. This work sought to understand how EGCG affects the phosphatidylinositol-3-kinase protein kinase B (PI3K/Akt) signaling pathway, which in turn causes apoptosis in H1299 lung cancer cells.

Methods

In this experiment, multiple dosages of EGCG were applied to five H1299 cells and five A549 cell lines for a duration of 72 h. Apoptotic pathways, cellular responses, and protein expression levels were investigated in relation to EGCG by morphological, biochemical, and proliferation/migration investigations.

Results

In H1299 and A549 cells, EGCG raised apoptosis rates and, in a dose-dependent way, hindered cell growth. The levels of phosphorylated Akt (p-Akt) and PI3K (p-PI3K) dramatically reduced following EGCG administration, despite no significant alterations in Akt and PI3K expressions. These results imply that EGCG inhibits the activation of the PI3K/Akt signaling pathway, which in turn causes apoptosis in H1299 and A549 cells.

Conclusion

The research provides insights into the effects of EGCG on proliferation and migratory inhibition, as well as highlighting its potential to induce apoptosis in lung cancer cells. These results support EGCG's promise as a therapeutic agent in the treatment of lung cancer and further our understanding of the processes underlying its anticancer activities.

Thymoquinone alleviates the accumulation of ROS and pyroptosis and promotes perforator skin flap survival through SIRT1/NF-κB pathway

Perforator flap transplantation is an important technique in flap reconstructive surgery, but flap necrosis limits its clinical effectiveness. Thymoquinone (TQ), a natural bioactive plant quinone found in black seed, exhibits anti-inflammatory, angiogenic, and antimicrobial properties. This study investigates the therapeutic effects of TQ in a perforator flap model through in vivo and in vitro experiments. Human umbilical vein endothelial cells (HUVECs) were treated with Tert-butyl Hydroperoxide (TBHP) to simulate an in vitro flap model and were then treated with TQ. In vivo experiments used a rat perforator flap model, and vascularization was assessed using Doppler ultrasound on days 3 and 7 after flap creation. On day 7 post-surgery, flap samples were collected to evaluate vascularity, reactive oxygen species, apoptosis and pyroptosis. Network pharmacology analysis was conducted to identify relevant signaling pathways, and molecular docking techniques were used to predict potential target binding sites. In vitro results showed that both TQ treatment and NLRP3 inhibitors reduced the expression of pyroptosis-related proteins. In vivo results indicated that the TQ-treated group had increased flap survival area, blood flow intensity, and microvascular density, while oxidative stress, apoptosis, and pyroptosis levels were reduced. Angiogenesis was enhanced, and expression of the SIRT1 protein was increased, while the p-P65/NF-κB/NLRP3 pathway was downregulated. After treatment with a SIRT1 inhibitor, flap survival rate and angiogenesis were reduced. These findings suggest that TQ improves perforator flap survival by inhibiting the NF-κB/NLRP3 pathway and promoting angiogenesis.

Next generation of neuromuscular blockade reversal agents

PURPOSE OF THE REVIEW

The purpose of this review is to explore emerging pharmacological strategies for neuromuscular blockade (NMB) reversal, focusing on their mechanisms of action, efficacy, and potential advantages over existing agents like sugammadex and neostigmine.

RECENT FINDINGS

Several novel reversal agents are under investigation: calabadions, synthetic molecular containers that effectively reverse aminosteroidal and benzylisoquinolinium neuromuscular blocking agents (NMBAs) with rapid clearance; l-cysteine adduction, a promising method for reversing ultrashort-acting NMBAs by accelerating metabolism; adamgammadex, a modified γ-cyclodextrin with improved binding to rocuronium and reduced hypersensitivity risks, showing noninferiority to sugammadex; acyclic cucurbit[n]urils, broad-spectrum reversal agents with superior water solubility and biocompatibility; and ClC-1 channel blockers, a novel approach that enhances muscle excitability rather than directly binding to NMBAs.

SUMMARY

Emerging NMBA reversal agents offer potential improvements in safety, efficacy, and broader NMBA compatibility. These alternatives to sugammadex and neostigmine show promise in preclinical and early clinical trials. Further studies are necessary to confirm their clinical applicability and regulatory approval.

The Role of PHLDA3 in Cancer Progression and Its Potential as a Therapeutic Target

Pleckstrin homology-like domain family A, member 3 (PHLDA3) is a p53-regulated tumor suppressor protein that suppresses AKT-mediated survival and oncogenic signaling. The PHLDA3 gene has garnered significant attention due to its multifaceted roles in tumorigenesis, metastasis, and invasion. This review explores the complex interactions between PHLDA3 and key cellular processes involved in cancer, emphasizing its regulatory mechanisms and clinical relevance. PHLDA3 has been found to be a critical regulator of metastatic pathways, particularly through its influence on the epithelial-mesenchymal transition (EMT) and in cellular invasion. Its interactions with pivotal signaling pathways, such as the Phosphoinositide 3-kinases/Protein kinase B (PI3K/AKT), p53, and Wnt/β-catenin pathways, highlight its multifunctional roles in various cancer types. Additionally, we discuss the potential of PHLDA3 as both a prognostic biomarker and a therapeutic target, offering new insights into its potential in treating advanced-stage malignancies. This review provides a detailed analysis of the role of PHLDA3 in cancer progression, including metastasis and invasion, underscoring its therapeutic potential.

Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study

Background

Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem.

Objective

This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions.

Methods

The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test.

Results

ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95% CI 8-8.4), compared with the original PLSs (median SMOG score 13.1, 95% CI 12.9-13.4). These PLSs had a median word count of 240 (95% CI 232-248) compared with the original PLSs' median word count of 364 (95% CI 339-388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95% CI 11.1-12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95% CI 8.4-8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs.

Conclusions

ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is accurate, reliable, and culturally relevant for different audiences.

Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance

Background

Rural health care providers face unique challenges such as limited specialist access and high patient volumes, making accurate diagnostic support tools essential. Large language models like GPT-3 have demonstrated potential in clinical decision support but remain understudied in pediatric differential diagnosis.

Objective

This study aims to evaluate the diagnostic accuracy and reliability of a fine-tuned GPT-3 model compared to board-certified pediatricians in rural health care settings.

Methods

This multicenter retrospective cohort study analyzed 500 pediatric encounters (ages 0-18 years; n=261, 52.2% female) from rural health care organizations in Central Louisiana between January 2020 and December 2021. The GPT-3 model (DaVinci version) was fine-tuned using the OpenAI application programming interface and trained on 350 encounters, with 150 reserved for testing. Five board-certified pediatricians (mean experience: 12, SD 5.8 years) provided reference standard diagnoses. Model performance was assessed using accuracy, sensitivity, specificity, and subgroup analyses.

Results

The GPT-3 model achieved an accuracy of 87.3% (131/150 cases), sensitivity of 85% (95% CI 82%-88%), and specificity of 90% (95% CI 87%-93%), comparable to pediatricians' accuracy of 91.3% (137/150 cases; P=.47). Performance was consistent across age groups (0-5 years: 54/62, 87%; 6-12 years: 47/53, 89%; 13-18 years: 30/35, 86%) and common complaints (fever: 36/39, 92%; abdominal pain: 20/23, 87%). For rare diagnoses (n=20), accuracy was slightly lower (16/20, 80%) but comparable to pediatricians (17/20, 85%; P=.62).

Conclusions

This study demonstrates that a fine-tuned GPT-3 model can provide diagnostic support comparable to pediatricians, particularly for common presentations, in rural health care. Further validation in diverse populations is necessary before clinical implementation.

Identifying healthcare needs with patient experience reviews using ChatGPT

BACKGROUND

Valuable findings can be obtained through data mining in patients' online reviews. Also identifying healthcare needs from the patient's perspective can more accurately improve the quality of care and the experience of the visit. Thereby avoiding unnecessary waste of health care resources. The large language model (LLM) can be a promising tool due to research that demonstrates its outstanding performance and potential in directions such as data mining, healthcare management, and more.

OBJECTIVE

We aim to propose a methodology to address this problem, specifically, the recent breakthrough of LLM can be leveraged for effectively understanding healthcare needs from patient experience reviews.

METHODS

We used 504,198 reviews collected from a large online medical platform, haodf.com. We used the reviews to create Aspect Based Sentiment Analysis (ABSA) templates, which categorized patient reviews into three categories, reflecting the areas of concern of patients. With the introduction of thought chains, we embedded ABSA templates into the prompts for ChatGPT, which was then used to identify patient needs.

RESULTS

Our method has a weighted total precision of 0.944, which was outstanding compared to the direct narrative tasks in ChatGPT-4o, which have a weighted total precision of 0.890. Weighted total recall and F1 scores also reached 0.884 and 0.912 respectively, surpassing the 0.802 and 0.843 scores for "direct narratives in ChatGPT." Finally, the accuracy of the three sampling methods was 91.8%, 91.7%, and 91.2%, with an average accuracy of over 91.5%.

CONCLUSIONS

Combining ChatGPT with ABSA templates can achieve satisfactory results in analyzing patient reviews. As our work applies to other LLMs, we shed light on understanding the demands of patients and health consumers with novel models, which can contribute to the agenda of enhancing patient experience and better healthcare resource allocations effectively.

Impact of Clinical Decision Support Systems on Medical Students' Case-Solving Performance: Comparison Study with a Focus Group

Background

Health care practitioners use clinical decision support systems (CDSS) as an aid in the crucial task of clinical reasoning and decision-making. Traditional CDSS are online repositories (ORs) and clinical practice guidelines (CPG). Recently, large language models (LLMs) such as ChatGPT have emerged as potential alternatives. They have proven to be powerful, innovative tools, yet they are not devoid of worrisome risks.

Objective

This study aims to explore how medical students perform in an evaluated clinical case through the use of different CDSS tools.

Methods

The authors randomly divided medical students into 3 groups, CPG, n=6 (38%); OR, n=5 (31%); and ChatGPT, n=5 (31%); and assigned each group a different type of CDSS for guidance in answering prespecified questions, assessing how students' speed and ability at resolving the same clinical case varied accordingly. External reviewers evaluated all answers based on accuracy and completeness metrics (score: 1-5). The authors analyzed and categorized group scores according to the skill investigated: differential diagnosis, diagnostic workup, and clinical decision-making.

Results

Answering time showed a trend for the ChatGPT group to be the fastest. The mean scores for completeness were as follows: CPG 4.0, OR 3.7, and ChatGPT 3.8 (P=.49). The mean scores for accuracy were as follows: CPG 4.0, OR 3.3, and ChatGPT 3.7 (P=.02). Aggregating scores according to the 3 students' skill domains, trends in differences among the groups emerge more clearly, with the CPG group that performed best in nearly all domains and maintained almost perfect alignment between its completeness and accuracy.

Conclusions

This hands-on session provided valuable insights into the potential perks and associated pitfalls of LLMs in medical education and practice. It suggested the critical need to include teachings in medical degree courses on how to properly take advantage of LLMs, as the potential for misuse is evident and real.

The global trend of intravenous anesthesia and tumors: a bibliometric and visualized study

BACKGROUND

Through bibliometric analysis, this paper summarizes the growing literature and analyses the development trends and hot spots in the research field of the association between intravenous anesthesia and tumors.

METHODS

A literature overview was conducted using relevant articles retrieved from the Web of Science Core Collection published between 1991 and 2024. Bibliometric websites and tools (VOSviewer, CiteSpace, and bibliometric R package) are used to analyze the co-occurrence of keywords and reference citations, detailing countries, institutions, authors, references, journals, and keywords.

RESULTS

A total of 1198 relevant articles were included in the study. The USA, China, and Germany have the largest number of publications, with the USA and China having the most interagency cooperation and Germany relatively less cooperation. The institution that publishes the most articles is the University of National Defense Medical Center, and the journal that publishes the most articles is Anesthesiology. The five most productive authors are Li J., Liu J., Zhang B., Yu X.J., and Xu J. "Surgery", "anaesthesia", and "propofol" are the most common keywords. In recent years, research has focused mainly on intravenous anesthesia and tumor survival.

CONCLUSION

Both Western countries and China have made outstanding contributions to intravenous anesthesia and tumors. In recent years, the number of publications in China has steadily increased, and the quality and influence of these articles deserve recognition. Future research should focus on the key areas of intravenous anesthesia, tumor recurrence, and survival.

A Comparison of the Effect of Two Doses of Oral Melatonin as Premedication on Orientation Score, Induction Compliance, and Emergency Agitation of Children Undergoing Elective Surgeries: A Double-Blinded Randomized Trial

Background: Following sedation or general anesthesia, emergent agitation (EA) presents as a sequence of abrupt, complicated psychomotor problems marked by perceptual abnormalities, delusions, and disorientation. Studies have proved that melatonin significantly decreases the incidence of postoperative agitation in children after anesthesia. The primary objective of this study was to compare the effectiveness of two doses of oral melatonin as a premedication for orientation score, induction compliance, and emergency agitation of children undergoing surgeries. Methods: In this double-blinded randomized controlled trial, 126 children, aged 4-14, of either sex, with an ASA I or II, scheduled for elective surgery were randomly assigned to get either melatonin 0.4 mg/kg (Group M4) or melatonin 0.2 mg/kg (Group M2), with 63 kids in each group. All children have had the same anesthetic strategy. As a primary outcome, orientation score, induction compliance to intravenous induction anesthesia, and decreased emergency agitation were assessed. Results: Both groups were comparable in terms of demographic characteristics and baseline data. Orientation scores were similar between the groups. Preoperatively, all patients were oriented in both time and place. The two groups had no statistically significant difference according to induction compliance distribution (p=0.065). There was a statistically significant difference in agitation behavior after 5, 10, and 15 min postoperatively in M 4, 2, and total participants (p < 0.001). Conclusion: In pediatric surgical patients, the melatonin dosage does not affect children's compliance with induction but impacts their postoperative behavior by reducing the likelihood of agitation. Administering oral melatonin before surgery could potentially aid in managing postoperative delirium in children.

Multidisciplinary Integrative Medicine Approach for Cancer Patients: A Multicenter Retrospective Study

BACKGROUND

The use of complementary integrative medicine (CIM) by cancer patients is currently very common. The main reasons why patients turn to CIM are to improve quality of life (QoL) and support the immune system. Unfortunately, many patients rely on CIM self-prescription, neglecting the risk of interactions with anticancer treatments (ACTs). The primary objective is to demonstrate the feasibility of combining CIM and ACT in a multidisciplinary approach to improve the QoL of cancer patients and to reduce ACT's adverse events.

METHODS

Cancer patients were treated with CIM by expert physicians. CIM mainly consisted of vitamins C and D, the medicinal mushrooms blend U-CARE, and probiotics administered alone or in combination. The patients were followed-up by physicians and data were recorded in a detailed shared file.

RESULTS

A total of 54 cancer patients were treated with an integrative approach, especially during ACTs. The combination showed a good safety profile. No adverse events occurred in 92.6% of patients, whereas only 7.4% of patients experienced gastrointestinal or liver toxicity from the CIM approach. The main benefit of the CIM approach was improved fatigue and QoL, and this was mainly achieved by the concomitant use of polytherapy-based complementary medicine (PCM) and U-CARE. The toxicity improvement was mainly associated with the use of solely U-CARE.

CONCLUSIONS

These results highlight the feasibility of the CIM approach in cancer patients addressed by a multidisciplinary team of experts in the field. The patient-centered and evidence-based approach of CIM is an example of the comprehensive and coordinated strategy pursued by the EU in its programmatic document against cancer aiming to focus on the QoL of patients and to avoid potentially harmful CIM self-prescription.

Sequential Treatment with Regorafenib and Trifluridine/Tipiracil ± Bevacizumab in Refractory Metastatic Colorectal Cancer in Community Clinical Practice in the USA

Background: Regorafenib (R) and Trifluridine/Tipiracil ± bevacizumab (T) are approved for treating refractory metastatic colorectal cancer (mCRC) but their optimal sequence is unclear. This study describes the characteristics/clinical outcomes of patients with mCRC in U.S. clinical practice treated sequentially with R-T or T-R. Methods: A retrospective cohort study of 818 patients treated with R-T or T-R between January 2015 and November 2022 was conducted using an electronic health record-derived database. The primary objective was to describe the demographic/clinical characteristics and biomarker status of patients treated with R-T or T-R, stratified by treatment line/age. Secondary objectives were to evaluate/estimate the frequency of neutropenia and myelosuppression-related treatments, the number/type of subsequent therapies, time to treatment discontinuation (TTD), and overall survival (OS). Results: Baseline characteristics were similar among patients who received R-T (n = 393) or T-R (n = 425). Lower rates of moderate/severe neutropenia (26%/12% vs. 32%/16%) and granulocyte colony-stimulating factor/erythropoietin use (22% vs. 24%) were observed with R-T versus T-R. The median TTD was 8.7 months and 8.5 months with R-T versus 8.1 months and 7.9 months with T-R as third- and fourth-line treatment, respectively. The median OS was 13.1 months and 11.6 months with R-T versus 11.5 months and 10.3 months with T-R as third- and fourth-line treatment, respectively. Conclusions: This study did not show a statistically significant difference in OS with R-T versus T-R. Although limited by its retrospective nature, the study suggested R-T may be preferable to T-R given the observed reduction in neutropenia/myelosuppression-related treatments.

Comparative analysis of traditional and integrated approaches to radiology training for residents

BACKGROUND

The study aims to conduct a comparative analysis of traditional and integrated approaches to radiology teaching in order to evaluate the effectiveness of novel educational methods.

METHODS

The study was conducted in Shenzhen, China, between January and December 2023. It involved 100 radiology residents who were randomly assigned to either a traditional training (TT) group or an integrated training (IT) group. The average age of participants was 28 years.

RESULTS

The TT group received conventional lectures and practical training, while the IT group used simulation software, interactive platforms, and artificial intelligence (AI) tools. The analysis revealed that the mean score of the IT group in the theoretical knowledge test was 170.3 ± 15.1, which is significantly higher than that of the TT group (155.7 ± 20.4; t = 4.21, p < 0.001). In the practical skills test, the IT group scored 160.7 ± 22.4, while the TT group scored 135.8 ± 25.6 (t = 5.13, p < 0.001).

CONCLUSIONS

The findings of the study indicate a significant advantage of an integrated approach to radiology teaching over conventional methods. The integration of modern technologies into the learning process has been shown to enhance both short- and long-term educational outcomes in medical education. This finding is of practical significance for educational institutions in this field. It is recommended that integrated teaching methods be introduced in order to improve the quality of specialist training.

CLINICAL TRIAL NUMBER

Not applicable.

Effects of Zofenopril and Thymoquinone in Cyclophosphamide-Induced Urotoxicity and Nephrotoxicity in Rats; The Value of Their Anti-Inflammatory and Antioxidant Properties

Objective

The study aimed to investigate whether zofenopril (ZOF), thymoquinone (TQ), or their co-administration effectively ameliorates urotoxicity and nephrotoxicity following cyclophosphamide (CPH) treatment.

Methods

A total of 48 Wister Albino female rats were divided into six groups each of eight rats; negative control (NC), positive control (PC), mesna (MS), ZOF, TQ, and ZOF+TQ groups. Normal saline, mesna, ZOF-15mg/kg, TQ-80mg/kg, and their combination were given orally for 19 days to the groups NC, MS, ZOF, TQ, and ZOF+TQ respectively. On the 17th day, a single dose of CPH 200 mg/kg was given intraperitoneally for all the groups except the NC group. Urine was collected over 24 hours before animal scarification for urinalysis. After scarification, blood, and kidney tissue were obtained for assessment of conventional kidney function parameters, novel kidney injury biomarkers, pro-inflammatory cytokines, oxidative status, complete blood count (CBC), and histopathological examination.

Results

CPH disturbed the urinary excretion of urea, creatinine, and protein, and significantly elevated novel biomarkers for kidney injury including cystatin-C (Cys-C) (p=0.019) and markedly kidney injury molecule-1 (KIM-1) (p=0.27), the semiquantitative measurement of hematuria revealed significant elevation of hematuria score (p=0.0002), urine pus and protein (p=0.0005). Additionally, CBC-derived inflammatory biomarkers including neutrophil-lymphocyte ratio (NLR) (p=0.001), neutrophil-monocyte ratio (NMR) (p=0.0004), pro-inflammatory cytokine interleukin (IL)-6 (p=0.016) and tumor necrosis factor (TNF)-α (p<=0.007), total antioxidant capacity (TAC) (p<0.0001) were significantly increased. Evidence of obvious histopathological structural alteration was noticed in kidney tissue and bladder urothelium in CPH-treated animals. ZOF, TQ, and their co-treatment significantly prevented these deleterious effects associated with CPH treatment.

Conclusion

This study demonstrated that ZOF and TQ provided uroprotective and nephroprotective effects against CPH-induced nephrotoxicity by reducing kidney injury biomarkers, and CBC-derived inflammatory markers, restoring antioxidant capacity, and improving histopathological outcomes. The suggested mechanism involves the anti-inflammatory and antioxidant activity of TQ and the sulfhydryl-angiotensin converting enzyme inhibitor ZOF.

Prediction of prognosis and immune response in lung adenocarcinoma by peroxisome related-lncRNA

BACKGROUND

Lung adenocarcinoma (LUAD) represents the primary tissue subtype of lung cancer, characterized by a significant mortality rate and unfavorable prognosis. Long non-coding RNAs (lncRNAs) serve as critical functional units in the initiation and progression of tumors. Peroxisomes, now recognized as vital organelles in tumor immune metabolism, have garnered considerable attention in recent research. Some LncRNAs regulate peroxisome protein conformation through the mechanism of RNA molecular chaperones to maintain its normal function. This study aims to investigate the role of peroxisome-related lncRNA signatures in predicting clinical outcomes and immunotherapy efficacy, as well as their correlation with drug sensitivity.

METHODS

The genomic and clinical information stemmed from the Cancer Genome Atlas (TCGA) database, while the peroxisome-related genes came from relevant studies. We developed prognostic features through co-expression analysis, Cox regression analysis, and LASSO analysis. Then we classified patients into high-risk and low-risk cohorts and performed extensive model validations to assess the prognostic significance of the signature. We used RT-qPCR to detect the expression of six peroxisome-associated lncRNAs in lung adenocarcinoma cells. Following this, we analyzed immune-related functions and tumor mutation burden (TMB). Lastly, we identified potential drugs and evaluated the drug sensitivity for LUAD.

RESULTS

We identified six peroxisome-related lncRNAs that serve as prognostic biomarkers. Our analysis revealed that high-risk patients exhibited decreased survival rates and increased mortality. Independent prognostic evaluations, receiver operating characteristic (ROC) curves, and bar charts demonstrated that these peroxisome-related lncRNAs can effectively predict patient outcomes. RT-qPCR results also indicated that these peroxisomes showed significant differences in mRNA expression in lung normal and lung adenocarcinoma cells. Moreover, in high-risk individuals, we observed reduced immune cell infiltration, inhibited immune functions, and an elevated tumor mutational burden (TMB). Consequently, high-risk patients faced a higher likelihood of immune evasion, resulting in diminished effectiveness of immunotherapy.

CONCLUSION

In conclusion, the six lncRNAs linked to peroxisomes can reliably forecast the prognosis of LUAD patients and may offer novel perspectives for clinical applications and immunotherapy.

Generative AI chatbots for reliable cancer information: Evaluating web-search, multilingual, and reference capabilities of emerging large language models

Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible LLMs-ChatGPT, Co-Pilot, Gemini, MetaAI, Claude, Grok, Perplexity-on three simple cancer-related queries across eight languages (336 responses: English, French, Chinese, Thai, Hindi, Nepali, Vietnamese, and Arabic). None of the 42 English responses contained clinically meaningful hallucinations, whereas 7 of 294 non-English responses did. 48 % (162/336) of responses included valid references, but 39 % of the English references were.com links reflecting quality concerns. English responses frequently exceeded an eighth-grade level, and many non-English outputs were also complex. These findings reflect substantial progress over the past 2-years but reveal persistent gaps in multilingual accuracy, reliable reference inclusion, referral practices, and readability. Ongoing benchmarking is essential to ensure LLMs safely support global health information dichotomy and meet online information standards.

Comparative Efficacy of Video Games Versus Midazolam in Reducing Perioperative Anxiety in Pediatric Patients: Systematic Review and Meta-Analysis

Background

Pediatric patients undergoing surgery frequently experience significant anxiety, which can result in adverse effects such as prolonged sedation and behavioral changes associated with pharmacological interventions such as oral midazolam. Video games offer a nonpharmacological distraction method that shows promise in alleviating procedural anxiety without significant adverse effects. However, the effectiveness of video games compared to midazolam in managing perioperative anxiety remains uncertain.

Objective

This study aimed to evaluate the effectiveness of video game interventions in reducing perioperative anxiety in pediatric patients undergoing general anesthesia.

Methods

We conducted a comprehensive search across PubMed, Embase, Web of Science, and the Cochrane Library, supplemented by reference screening. Primary outcomes included anxiety levels assessed during parent separation and mask induction procedures, while secondary outcomes encompassed emergence delirium, postoperative behavior, and length of stay in the postanesthesia care unit (PACU). The risk of bias was assessed using the Risk of Bias 2 scale. Data were synthesized descriptively and through meta-analysis, with the certainty of the evidence evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria.

Results

Six randomized controlled trials involving 612 participants were included in the analysis. Children who participated in video game interventions reported significantly lower anxiety levels during parent separation (standardized mean difference, SMD -0.31, 95% CI -0.50 to -0.12; P=.001), with high certainty, and during mask induction (SMD -0.29, 95% CI -0.52 to -0.05; P=.02), with moderate certainty, compared to those receiving oral midazolam. Additionally, significant differences in postoperative behavior changes in children were observed compared to oral midazolam (SMD -0.35, 95% CI -0.62 to -0.09; P=.008). Children in the video game intervention groups also had a shorter length of stay in the PACU (mean difference, MD -19.43 min, 95% CI -31.71 to -7.16; P=.002). However, no significant differences were found in emergence delirium (MD -2.01, 95% CI -4.62 to 0.59; P=.13).

Conclusions

Video game interventions were more effective than midazolam in reducing perioperative anxiety among pediatric patients, improving postoperative behavior, and shortening the length of stay in the PACU. However, video games alone did not outperform midazolam in managing emergence delirium. Further high-quality research is needed for more conclusive results.

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models

Background

Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize health care quality and accessibility. At a time when improving health care quality and access remains a critical concern for countries worldwide, the ability of these models to pass medical examinations is often cited as a reason to use them for medical training and diagnosis. However, the impact of their inevitable use as a self-diagnostic tool and their role in spreading health care misinformation has not been evaluated.

Objective

This study aims to assess the effectiveness of LLMs, particularly ChatGPT, from the perspective of an individual self-diagnosing to better understand the clarity, correctness, and robustness of the models.

Methods

We propose the comprehensive testing methodology evaluation of LLM prompts (EvalPrompt). This evaluation methodology uses multiple-choice medical licensing examination questions to evaluate LLM responses. Experiment 1 prompts ChatGPT with open-ended questions to mimic real-world self-diagnosis use cases, and experiment 2 performs sentence dropout on the correct responses from experiment 1 to mimic self-diagnosis with missing information. Humans then assess the responses returned by ChatGPT for both experiments to evaluate the clarity, correctness, and robustness of ChatGPT.

Results

In experiment 1, we found that ChatGPT-4.0 was deemed correct for 31% (29/94) of the questions by both nonexperts and experts, with only 34% (32/94) agreement between the 2 groups. Similarly, in experiment 2, which assessed robustness, 61% (92/152) of the responses continued to be categorized as correct by all assessors. As a result, in comparison to a passing threshold of 60%, ChatGPT-4.0 is considered incorrect and unclear, though robust. This indicates that sole reliance on ChatGPT-4.0 for self-diagnosis could increase the risk of individuals being misinformed.

Conclusions

The results highlight the modest capabilities of LLMs, as their responses are often unclear and inaccurate. Any medical advice provided by LLMs should be cautiously approached due to the significant risk of misinformation. However, evidence suggests that LLMs are steadily improving and could potentially play a role in health care systems in the future. To address the issue of medical misinformation, there is a pressing need for the development of a comprehensive self-diagnosis dataset. This dataset could enhance the reliability of LLMs in medical applications by featuring more realistic prompt styles with minimal information across a broader range of medical fields.

Recurrent Osteomyelitis in a Paediatric Patient with a Novel NTRK1 Mutation: A Case Report on Congenital Insensitivity to Pain with Anhidrosis

BACKGROUND

Congenital insensitivity to pain with anhidrosis (CIPA), also known as hereditary sensory and autonomic neuropathy type IV (HSAN IV), is an exceedingly rare genetic disorder characterized by the inability to perceive pain, inability to sweat, and various neurological and orthopaedic complications.

CASE PRESENTATION

This is a case report of a 3-year-old female patient as the first case in Latvia diagnosed with CIPA syndrome who repeatedly presented to Children's Clinical University Hospital (CCUH) in Riga, Latvia, with severe orthopaedic manifestations. The patient had repeated fractures, several surgeries, and extensive spread of the disease throughout the left leg, which caused significant functional impairment and decreased quality of life. Despite aggressive orthopaedic interventions, including surgical interventions and physical therapy, the patient's condition remained challenging to manage due to the inherent limitations posed by the insensitivity to pain. The Surgeon-Radiologist Council of Doctors discussed the patient's condition and clinical sequalae, deciding that reconstructive surgery is not feasible, and amputation is recommended.

CONCLUSIONS

Through this case report, we aim to highlight the unique orthopaedic challenges encountered in the management of CIPA patients, emphasizing the importance of a multidisciplinary approach involving orthopaedic surgeons, paediatricians, geneticists, and physiotherapists. Additionally, we discuss the need for further research to elucidate optimal management strategies and improve outcomes in this rare and complex patient population.

Seasonal Change in Microbial Diversity: Bile Microbiota and Antibiotics Resistance in Patients with Bilio-Pancreatic Tumors: A Retrospective Monocentric Study (2010-2020)

Background: Bilio-pancreatic tumors are a severe form of cancer with a high rate of associated mortality. These patients showed the presence of bacteria such as Escherichia coli and Pseudomonas spp. in the bile-pancreatic tract. Therefore, efficient antibiotic therapy is essential to reduce bacterial resistance and adverse events in cancer patients. Recent studies on the seasonality of infectious diseases may aid in developing effective preventive measures. This study examines the seasonal impact on the bile microbiota composition and the antibiotic resistance of its microorganisms in patients with hepato-pancreatic-biliary cancer. Methods: We retrospectively evaluated the effect of the seasonally from 149 strains isolated by 90 Italian patients with a positive culture of bile samples collected through endoscopic retrograde cholangiopancreatography between 2010 and 2020. Results: Across all seasons, the most frequently found bacteria were E. coli, Pseudomonas spp., and Enterococcus spp. Regarding antibiotic resistance, bacteria showed the highest resistance to 3GC, fluoroquinolones, aminoglycosides, fosfomycin, and piperacillin-tazobactam in the summer and the lowest resistance in the spring, except for carbapenems and colistin. Conclusions: Antibiotic resistance has negative effects in cancer patients who rely on antibiotics to prevent and treat infections. Knowing whether bacterial and fungal resistance changes with the seasons is key information to define adequate and more effective antibiotic therapy.

A systematic review of large language model (LLM) evaluations in clinical medicine

BACKGROUND

Large Language Models (LLMs), advanced AI tools based on transformer architectures, demonstrate significant potential in clinical medicine by enhancing decision support, diagnostics, and medical education. However, their integration into clinical workflows requires rigorous evaluation to ensure reliability, safety, and ethical alignment.

OBJECTIVE

This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine, highlighting their capabilities, limitations, and application trends.

METHODS

A comprehensive review of the literature was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and arXiv databases, encompassing both peer-reviewed and preprint studies. Studies were screened against predefined inclusion and exclusion criteria to identify original research evaluating LLM performance in medical contexts.

RESULTS

The results reveal a growing interest in leveraging LLM tools in clinical settings, with 761 studies meeting the inclusion criteria. While general-domain LLMs, particularly ChatGPT and GPT-4, dominated evaluations (93.55%), medical-domain LLMs accounted for only 6.45%. Accuracy emerged as the most commonly assessed parameter (21.78%). Despite these advancements, the evidence base highlights certain limitations and biases across the included studies, emphasizing the need for careful interpretation and robust evaluation frameworks.

CONCLUSIONS

The exponential growth in LLM research underscores their transformative potential in healthcare. However, addressing challenges such as ethical risks, evaluation variability, and underrepresentation of critical specialties will be essential. Future efforts should prioritize standardized frameworks to ensure safe, effective, and equitable LLM integration in clinical practice.

Oxidative Stress by H2O2 as a Potential Inductor in the Switch from Commensal to Pathogen in Oncogenic Bacterium Fusobacterium nucleatum

BACKGROUND

Fusobacterium nucleatum is a pathobiont that plays a dual role as both a commensal and a pathogen. The oral cavity typically harbors this anaerobic, Gram-negative bacterium. At the same time, it is closely linked to colorectal cancer due to its potential involvement in tumor progression and resistance to chemotherapy. The mechanism by which it transforms from a commensal to a pathogen remains unknown. For this reason, we investigated the role of oxidative status as an initiatory factor in changing the bacterium's pathogenicity profile.

METHODS

A clinical strain of F. nucleatum subsp. animalis biofilm was exposed to different oxidative stress levels through varying subinhibitory amounts of H2O2. Subsequently, we investigated the bacterium's behavior in vitro by infecting the HT-29 cell line. We evaluated bacterial colonization, volatile sulfur compounds production, and the infected cell's oxidative status by analyzing HMOX1, pri-miRNA 155, and 146a gene expression.

RESULTS

The bacterial colonization rate, dimethyl sulfide production, and pri-miRNA 155 levels all increased when stressed bacteria were used, suggesting a predominant pathogenic function of these strains.

CONCLUSIONS

The response of F. nucleatum to different oxidative conditions could potentially explain the increase in its pathogenic traits and the existence of environmental factors that may trigger the bacterium's pathogenicity and virulence.

LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases

Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their outputs. Opioid prescribing is one domain in which these biases can have serious implications, especially given the ongoing opioid epidemic and the need to balance effective pain management with addiction risk. We tested ten LLMs-both open-access and closed-source-on 1,000 acute-pain vignettes. Half of the vignettes were labeled as non-cancer and half as cancer. Each vignette was presented in 34 socio-demographic variations, including a control group without demographic identifiers. We analyzed the models' recommendations on opioids, anxiety treatment, perceived psychological stress, risk scores, and monitoring recommendations. Overall, yielding 3.4 million model-generated responses. Using logistic and linear mixed-effects models, we measured how these outputs varied by demographic group and whether a cancer diagnosis intensified or reduced observed disparities. Across both cancer and non-cancer cases, historically marginalized groups-especially cases labeled as individuals who are unhoused, Black, or identify as LGBTQIA+-often received more or stronger opioid recommendations, sometimes exceeding 90% in cancer settings, despite being labeled as high risk by the same models. Meanwhile, low-income or unemployed groups were assigned elevated risk scores yet fewer opioid recommendations, hinting at inconsistent rationales. Disparities in anxiety treatment and perceived psychological stress similarly clustered within marginalized populations, even when clinical details were identical. These patterns diverged from standard guidelines and point to model-driven bias rather than acceptable clinical variation. Our findings underscore the need for rigorous bias evaluation and the integration of guideline-based checks in LLMs to ensure equitable and evidence-based pain care.