Effectiveness of Generative Artificial Intelligence-Driven Responses to Patient Concerns in Long-Term Opioid Therapy: Cross-Model Assessment

Background: While long-term opioid therapy is a widely utilized strategy for managing chronic pain, many patients have understandable questions and concerns regarding its safety, efficacy, and potential for dependency and addiction. Providing clear, accurate, and reliable information is essential for fostering patient understanding and acceptance. Generative artificial intelligence (AI) applications offer interesting avenues for delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT's responses to common patient inquiries about opioid long-term therapy. Methods: An expert panel selected thirteen frequently asked questions regarding long-term opioid therapy based on the authors' clinical experience in managing chronic pain patients and a targeted review of patient education materials. Questions were prioritized based on prevalence in patient consultations, relevance to treatment decision-making, and the complexity of information typically required to address them comprehensively. We assessed comprehensibility by implementing the multimodal generative AI Copilot (Microsoft 365 Copilot Chat). Spanning three domains-pre-therapy, during therapy, and post-therapy-each question was submitted to GPT-4.0 with the prompt "If you were a physician, how would you answer a patient asking…". Ten pain physicians and two non-healthcare professionals independently assessed the responses using a Likert scale to rate reliability (1-6 points), accuracy (1-3 points), and comprehensibility (1-3 points). Results: Overall, ChatGPT's responses demonstrated high reliability (5.2 ± 0.6) and good comprehensibility (2.8 ± 0.2), with most answers meeting or exceeding predefined thresholds. Accuracy was moderate (2.7 ± 0.3), with lower performance on more technical topics like opioid tolerance and dependency management. Conclusions: While AI applications exhibit significant potential as a supplementary tool for patient education on opioid long-term therapy, limitations in addressing highly technical or context-specific queries underscore the need for ongoing refinement and domain-specific training. Integrating AI systems into clinical practice should involve collaboration between healthcare professionals and AI developers to ensure safe, personalized, and up-to-date patient education in chronic pain management.

The Histomorphology to Molecular Transition: Exploring the Genomic Landscape of Poorly Differentiated Epithelial Endometrial Cancers

The peremptory need to circumvent challenges associated with poorly differentiated epithelial endometrial cancers (PDEECs), also known as Type II endometrial cancers (ECs), has prompted therapeutic interrogation of the prototypically intractable and most prevalent gynecological malignancy. PDEECs account for most endometrial cancer-related mortalities due to their aggressive nature, late-stage detection, and poor response to standard therapies. PDEECs are characterized by heterogeneous histopathological features and distinct molecular profiles, and they pose significant clinical challenges due to their propensity for rapid progression. Regardless of the complexities around PDEECs, they are still being administered inefficiently in the same manner as clinically indolent and readily curable type-I ECs. Currently, there are no targeted therapies for the treatment of PDEECs. The realization of the need for new treatment options has transformed our understanding of PDEECs by enabling more precise classification based on genomic profiling. The transition from a histopathological to a molecular classification has provided critical insights into the underlying genetic and epigenetic alterations in these malignancies. This review explores the genomic landscape of PDEECs, with a focus on identifying key molecular subtypes and associated genetic mutations that are prevalent in aggressive variants. Here, we discuss how molecular classification correlates with clinical outcomes and can refine diagnostic accuracy, predict patient prognosis, and inform therapeutic strategies. Deciphering the molecular underpinnings of PDEECs has led to advances in precision oncology and protracted therapeutic remissions for patients with these untamable malignancies.

Mechanism of action of lncRNA-NEAT1 in immune diseases

NEAT1, a long non-coding RNA (lncRNA), is involved in assembling nuclear paraspeckles that have been found to impact various immune-related diseases, such as autoimmune diseases, allergic diseases, cancer immunity, sepsis, etc. In immune-related diseases, lncRNA-NEAT1 affects the activation, proliferation, and differentiation process of immune cells by interacting with transcription factors and miRNA (MicroRNA) to regulate an expression level in immune-related genes. It can also regulate the apoptosis and autophagy processes of immune cells by regulating inflammatory responses, interacting with apoptosis-related proteins, or regulating the expression of autophagy-related genes, thereby regulating the development of immune-related diseases. In recent years, a large number of researchers have found that the abnormal expression of lncRNA-NEAT1 has a great impact on the onset and progression of immune diseases, such as innate immunity after viral infection and the humoral immunity of T lymphocytes. In this paper, the specific mechanism of action and the function of lncRNA-NEAT1 in different immune-related diseases are sorted out and analyzed, to furnish a theoretical foundation for the study of the mechanism of action of immune cells.

Building Consensus on the Relevant Criteria to Screen for Depressive Symptoms Among Near-Centenarians and Centenarians: Modified e-Delphi Study

BACKGROUND

The number of centenarians worldwide is expected to increase dramatically, reaching 3.4 million by 2050 and >25 million by 2100. Despite these projections, depression remains a prevalent yet underdiagnosed and undertreated condition among this population that carries significant health risks.

OBJECTIVE

This study aimed to identify and achieve consensus on the most representative signs and symptoms of depression in near-centenarians and centenarians (aged ≥95 years) through an e-Delphi study with an international and interdisciplinary panel of experts. Ultimately, the outcomes of this study might help create a screening instrument that is specifically designed for this unique population.

METHODS

A modified e-Delphi study was carried out to achieve expert consensus on depressive symptoms in near-centenarians and centenarians. A panel of 28 international experts was recruited. Consensus was defined as 70% agreement on the relevance of each item. Data were collected through a web-based questionnaire over 3 rounds. Experts rated 104 items that were divided into 24 dimensions and 80 criteria to identify the most representative signs and symptoms of depression in this age group.

RESULTS

The panel consisted of experts from various countries, including physicians with experience in old age psychiatry or geriatrics as well as nurses and psychologists. The response rate remained consistent over the rounds (20/28, 71% to 21/28, 75%). In total, 4 new dimensions and 8 new criteria were proposed by the experts, and consensus was reached on 86% (24/28) of the dimensions and 80% (70/88) of the criteria. The most consensual potentially relevant dimensions were lack of hope (21/21, 100%), loss of interest (27/28, 96%), lack of reactivity to pleasant events (27/28, 96%), depressed mood (26/28, 93%), and previous episodes of depression or diagnosed depression (19/21, 90%). In addition, the most consensual potentially relevant criteria were despondency, gloom, and despair (25/25, 100%); depressed (27/27, 100%); lack of reactivity to pleasant events or circumstances (28/28, 100%); suicidal ideation (28/28, 100%); suicide attempt(s) (28/28, 100%); ruminations (27/28, 96%); recurrent thoughts of death or suicide (27/28, 96%); feelings of worthlessness (25/26, 96%); critical life events (20/21, 95%); anhedonia (20/21, 95%); loss of interest in activities (26/28, 93%); loss of pleasure in activities (26/28, 93%); and sadness (24/26, 92%). Moreover, when assessing depression in very old age, the duration, number, frequency, and severity of signs and symptoms should also be considered, as evidenced by the high expert agreement.

CONCLUSIONS

The classification of most elements as relevant highlights the importance of a multidimensional approach for optimal depression screening among individuals of very old age. This study offers a first step toward improving depression assessment in near-centenarians and centenarians. The development of a more adapted screening tool could improve early detection and intervention, enhancing the quality of mental health care for this population.

A bibliometric analysis of perioperative medicine and artificial intelligence

BACKGROUND

Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity.

AIM

This bibliometric analysis aims to analyse trends, contributions, collaborations and research hotspots in artificial intelligence and perioperative medicine.

METHODS

A Scopus search on 11 October 2024 identified articles on artificial intelligence in perioperative medicine. Relevant peer-reviewed studies were screened by two reviewers, with a third resolving discrepancies. Data were analysed using VOSviewer, Biblioshiny and Microsoft Excel.

RESULTS

A total of 240 articles were included; 84% of articles were published after 2018, indicating rapid recent growth. The United States, China and Italy led contributions. Single-country publications comprised 76.6% of the dataset, reflecting limited international collaboration. Key research areas included perioperative risk prediction, intraoperative monitoring, blood management and echocardiography.

CONCLUSION

Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential.

Ethical engagement with artificial intelligence in medical education

The integration of large language models (LLMs) in medical education offers both opportunities and challenges. While these artificial intelligence (AI)-driven tools can enhance access to information and support critical thinking, they also pose risks like potential overreliance and ethical concerns. To ensure ethical use, students and instructors must recognize the limitations of LLMs, maintain academic integrity, and handle data cautiously, and instructors should prioritize content quality over AI detection methods. LLMs can be used as supplementary aids rather than primary educational resources, with a focus on enhancing accessibility and equity and fostering a culture of feedback. Institutions should create guidelines that align with their unique educational values, providing clear frameworks that support responsible LLM usage while addressing risks associated with AI in education. Such guidelines should reflect the institution's pedagogical mission, whether centered on clinical practice, research, or a mix of both, and should be adaptable to evolving educational technologies.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine

PURPOSE

To show the value of custom methods, namely Retrieval Augmented Generation (RAG)-based Large Language Models (LLMs) and Agentic Augmentation, over standard LLMs in delivering accurate information using an anterior cruciate ligament (ACL) injury case.

METHODS

A set of 100 questions and answers based on the 2022 AAOS ACL guidelines were curated. Closed-source (open AI GPT4/GPT 3.5 and Anthropic's Claude3) and open-source models (LLama3 8b/70b and Mistral 8×7b) were asked questions in base form and again with AAOS guidelines embedded into a RAG system. The top-performing models were further augmented with artificial intelligence (AI) agents and reevaluated. Two fellowship-trained surgeons blindly evaluated the accuracy of the responses of each cohort. Recall-Oriented Understudy of Gisting Evaluation and Metric for Evaluation of Translation with Explicit Ordering scores were calculated to assess semantic similarity in the response.

RESULTS

All noncustom LLM models started below 60% accuracy. Applying RAG improved the accuracy of every model by an average 39.7%. The highest performing model with just RAG was Meta's open-source Llama3 70b (94%). The highest performing model with RAG and AI agents was Open AI's GPT4 (95%).

CONCLUSIONS

RAG improved accuracy by an average of 39.7%, with the highest accuracy rate of 94% in the Meta Llama3 70b. Incorporating AI agents into a previously RAG-augmented LLM improved ChatGPT4 accuracy rate to 95%. Thus, Agentic and RAG augmented LLMs can be accurate liaisons of information, supporting our hypothesis.

CLINICAL RELEVANCE

Despite literature surrounding the use of LLM in medicine, there has been considerable and appropriate skepticism given the variably accurate response rates. This study establishes the groundwork to identify whether custom modifications to LLMs using RAG and agentic augmentation can better deliver accurate information in orthopaedic care. With this knowledge, online medical information commonly sought in popular LLMs, such as ChatGPT, can be standardized and provide relevant online medical information to better support shared decision making between surgeon and patient.

Classifications and treatment management of fragility fracture of the pelvis: A scoping review

BACKGROUND

Fragility fractures of the pelvis (FFP) present a growing challenge in aging populations. However, standardized classifications and treatment guidelines remain scarce.

OBJECTIVE

This scoping review examines the application of fracture classifications, treatment strategies, and outcome evaluations for FFP, identifying gaps in the literature, and suggesting directions for future research.

METHODS

A systematic search of multiple electronic databases yielded 117 studies discussing FFP names, classifications, treatment approaches, and outcomes. Data extraction focused on study characteristics, classification systems, treatment details, outcomes, and follow-up periods. Residual analysis using the Chi-square test assessed statistical associations and underrepresentation.

RESULTS

The FFP classification was the most common (51.3%), with additional treatment indicators focused on immobility (44.4%) and pain assessment (using the Visual Analog Scale [VAS] or Numeric Rating Scale [NRS], 37.6%), consistent with existing guidelines. In contrast, the sacral insufficient fractures were statistically associated with pain indications but lacked corresponding classification application. Initial management typically involved conservative or observation period. Regarding the management indications and outcomes, surgical interventions were categorized into osteosynthesis and sacroplasty. Outcome evaluations often incorporated mobility and functional status (59.0%), hospitalization length (49.6%), mortality rates (41.0%), and post-treatment living conditions (41.0%). Patient recovery was assessed through VAS scores (59.0%) and Activities of Daily Living Patient-Reported Outcomes (ADL-PROs, 34.2%). However, inconsistencies in standardized outcomes, particularly in sacroplasty studies, hinder comparative analysis.

CONCLUSION

FFP classifications, along with pain and mobility assessments, were frequently applied as management indicators for FFP. Standardizing treatment indications and establishing consistent outcome measures, including the evidenced gap treatments (sacral insufficient fracture and cement augmentation), could significantly improve comparability across studies.

A Joint LLM-KG System for Disease Q&A

Medical question answer (QA) assistants respond to lay users' health-related queries by synthesizing information from multiple sources using natural language processing and related techniques. They can serve as vital tools to alleviate issues of misinformation, information overload, and complexity of medical language, thus addressing lay users' information needs while reducing the burden on healthcare professionals. QA systems, the engines of such assistants, have often used large language models (LLMs) or knowledge graphs (KG), though the approaches could be complementary. LLM-based QA systems excel at understanding complex questions and providing well-formed answers but are prone to factual mistakes. KG-based QA systems, which represent facts well, are mostly limited to answering short-answer questions with pre-created templates. While a few studies have used both LLM and KG for text-based QA, the approaches are still prone to incomplete or inaccurate answers. Extant QA systems also have limitations in terms of automation and performance. We address these challenges by designing a novel, automated disease QA system named Disease Guru-Long-Form Question Answer (DG-LFQA), which effectively utilizes both LLM and KG techniques through a joint reasoning approach to answer disease-related questions appropriate for lay users. Our evaluation of the system using a range of quality metrics demonstrates its efficacy over related baseline systems.

Do Ophthalmology Journals Have AI Policies for Manuscript Writing?

PURPOSE

To assess the prevalence of artificial intelligence (AI) usage policies in manuscript writing in PubMed-indexed ophthalmology journals and examine the relationship between the adoption of these policies and journal characteristics.

DESIGN

Cross-sectional study.

SUBJECTS

PubMed-indexed ophthalmology journals.

MAIN OUTCOME MEASURES

Prevalence of policies in journal guidelines regarding the use of AI in manuscript writing.

METHODS

We reviewed the guidelines of 84 ophthalmology journals indexed in PubMed to determine the presence of AI-use policies for manuscript generation. We further compared journal metrics, such as CiteScore, Journal Impact Factor (JIF), Journal Citation Indicator (JCI), Source Normalized Impact per Paper (SNIP), and SCImago Journal Rank (SJR), between journals with and without AI policies. Additionally, we analyzed the association between AI policy adoption and journal characteristics, such as MEDLINE indexing and society affiliation.

RESULTS

Among the 84 journals, 53 (63.1%) had AI policies for manuscript generation, with no significant changes observed during the study period. Journals indexed in MEDLINE were significantly more likely to have AI policies (68.8%) than non-MEDLINE-indexed journals, where no AI policies were found (0%) (P = .0008). There was no significant difference in AI policy adoption between society-affiliated (62.7%) and unaffiliated journals (64.7%) (P = .84). Journals with AI policies had significantly higher metrics, including CiteScore, SNIP, SJR, JIF, and JCI (P < .05).

CONCLUSIONS

While many ophthalmology journals have adopted AI policies, the lack of guidelines in over one-third of journals highlights a critical need for consistent and comprehensive AI policies, particularly as the AI landscape rapidly advances.

A randomized controlled trial on evaluating clinician-supervised generative AI for decision support

BACKGROUND

The integration of generative artificial intelligence (AI) as clinical decision support systems (CDSS) into telemedicine presents a significant opportunity to enhance clinical outcomes, yet its application remains underexplored.

OBJECTIVE

This study investigates the efficacy of one of the most common generative AI tools, ChatGPT, for providing clinical guidance during cardiac arrest scenarios.

METHODS

We examined the performance, cognitive load, and trust associated with traditional methods (paper guide), autonomous ChatGPT, and clinician-supervised ChatGPT, where a clinician supervised the AI recommendations. Fifty-four subjects without medical backgrounds participated in randomized controlled trials, each assigned to one of three intervention groups: paper guide, ChatGPT, or supervised ChatGPT. Participants completed a standardized CPR scenario using an Augmented Reality (AR) headset, and performance, physiological, and self-reported metrics were recorded.

MAIN FINDINGS

Results indicate that the Supervised-ChatGPT group showed significantly higher decision accuracy compared to the paper guide and ChatGPT groups, although the scenario completion time was longer. Physiological data showed a reduced LF/HF ratio in the Supervised-ChatGPT group, suggesting potentially lower cognitive load. Trust in AI was also highest in the supervised condition. In one instance, ChatGPT suggested a risky option, highlighting the need for clinician supervision.

CONCLUSION

Our findings highlight the potential of supervised generative AI to enhance decision-making accuracy and user trust in emergency healthcare settings, despite trade-offs with response time. The study underscores the importance of clinician oversight and the need for further refinement of AI systems to improve safety. Future research should explore strategies to optimize AI supervision and assess the implementation of these systems in real-world clinical settings.

Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology

Integrating Artificial Intelligence (AI) through Natural Language Processing (NLP) can improve veterinary medical oncology clinical record analytics. Named Entity Recognition (NER), a critical component of NLP, can facilitate efficient data extraction and automated labelling for research and clinical decision-making. This study assesses the efficacy of the Bio-Epidemiology-NER (BioEN), an open-source NER developed using human epidemiological and medical data, on veterinary medical oncology records. The NER's performance was compared with manual annotations by a veterinary medical oncologist and a veterinary intern. Evaluation metrics included Jaccard similarity, intra-rater reliability, ROUGE scores, and standard NER performance metrics (precision, recall, F1-score). Results indicate poor direct translatability to veterinary medical oncology record text and room for improvement in the NER's performance, with precision, recall, and F1-score suggesting a marginally better alignment with the oncologist than the intern. While challenges remain, these insights contribute to the ongoing development of AI tools tailored for veterinary healthcare and highlight the need for veterinary-specific models.

Potential influence of different peri-operative analgesic regimens on tumour biology and outcome after oncologic surgery: A narrative review

The management of peri-operative pain is one of the pillars of anaesthesia and is of particular importance in patients undergoing surgery for solid malignant tumours. Amongst several options, the most commonly employed analgesic regimens involve opioids, NSAIDs and regional anaesthesia techniques with different local anaesthetics. In recent years, several research reports have tried to establish a connection between peri-operative anaesthesia care and outcome after cancer surgery. Experimental studies have indicated that certain pain management substances may influence cancer progression, mainly by modifying the tumour's response to surgical stress and peri-operative inflammation. However, these promising in-vitro and in-vivo data have yet to be confirmed by randomised clinical trials. The reason for this might lie with the nature of tumour biology itself, and in the diversity of patient and tumour phenotypes. In a translational approach, future research should therefore concentrate on patient and tumour-related factors or biomarkers, which might either influence the tumour and its microenvironment or predict potential responses to interventions, including the choice of the analgesic. This might not only be relevant for the daily practice of clinical anaesthesia, but would also be of great importance for patients undergoing cancer surgery, who might be able to receive an individualised anaesthetic regimen based on their phenotypic profile.

Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective

Purpose This survey aims to explore the attitudes of ophthalmologist oculoplastic surgeons in the United Kingdom (UK) towards specific advances in artificial intelligence (AI). Methods A web-based anonymised survey was distributed to oculoplastic surgeons in the UK between October 2023 and March 2024. The survey evaluated attitudes towards specific AI advances using a five-point scale. Questions were designed to ascertain understanding, application, as well as attitudes towards anticipated future developments, barriers and challenges of AI within the current oculoplastic clinical landscape. Results In total, 77 survey responses were analysed from oculoplastic surgeons practising around the UK. The majority of the responses were from oculoplastic consultants (64%, 44/69), with other responses from oculoplastic fellows (12%, 8/69), ophthalmology consultants (10%, 7/69), speciality training surgeons (6%, 4/69) and other ophthalmology clinicians (9%). The responses highlighted that there is a widespread level of subjective understanding of the topic of AI, a low level of current AI in clinical practice and an anticipation that AI will have a noticeable impact in the next one to five years (45%, 29/64) or five to 10 years (36%, 23/64) in the UK. Specific applications of applications of AI are also discussed in further detail. Conclusions Oculoplastic surgeons feel that AI will have an imminent role in this speciality, but there is a low level of self-rated knowledge and low current use of AI in clinical practice. Given the potential of this technology, further resources need to be invested into equipping Oculoplastic surgeons to feel more prepared for this technological revolution.

Interplay between miRNA expression and glucose metabolism in oral squamous cell carcinoma

OBJECTIVE

Given the urgent need for improved diagnostic and therapeutic strategies in oral squamous cell carcinoma (OSCC), this review aims to explore the intricate interplay between OSCC and alterations in glucose metabolism, with a particular focus on the pivotal role of microRNAs (miRNAs) in this context.

MATERIAL AND METHODS

Data were extracted from a vast literature survey by using PubMed, Embase, and Web of Science search engines with relevant keywords.

RESULTS

In OSCC, miRNAs exert regulatory control over the expression of genes involved in glucose metabolism pathways. Dysregulation of specific miRNAs has been implicated in the modulation of key glycolytic enzymes and glucose transporters, intracellular signaling cascades, and interaction with transcription factors, all of which collectively affect glucose uptake and glycolysis, contributing significantly to the observed metabolic alterations in OSCC cells.

CONCLUSION

A comprehensive understanding of these intricate molecular interactions holds significant promise for the development of targeted therapeutic interventions and refined diagnostic approaches to treat OSCC patients.

Irisin reprograms microglia through activation of STAT6 and prevents cognitive dysfunction after surgery in mice

Postoperative cognitive dysfunction (POCD) is common in the aged population and associated with poor clinical outcomes. Irisin, an endogenous molecule that mediates the beneficial effects of exercise, has shown neuroprotective potential in several models of neurological diseases. Here we show that preoperative serum level of irisin is reduced in dementia patients over the age of 70. Comprehensive proteomics analysis reveals that deletion of irisin affects the nervous and immune systems, and reduces the expression of complement proteins. Systemically administered irisin penetrates the blood-brain barrier in mice, targets the microglial integrin αVβ5 receptor, activates signal transducer and activator of transcription 6 (STAT6), induces microglia reprogramming to the M2 phenotype, and improves immune microenvironment in LPS-induced neuroinflammatory mice. Finally, prophylactic administration of irisin prevents POCD-like behavior, particularly early cognitive dysfunction. Our findings provide new insights into the direct regulation of the immune microenvironment by irisin, and reveal that recombinant irisin holds great promise as a novel therapy for preventing POCD and other neuroinflammatory disorders. SUMMARY: Our findings reveal molecular and cellular mechanisms of irisin on neuroinflammation, and show that prophylactic administration of irisin prevents POCD-like behavior, particularly early cognitive dysfunction.

Introduction to Artificial Intelligence for General Surgeons: A Narrative Review

Artificial intelligence (AI) has rapidly progressed in the last decade and will inevitably become incorporated into trauma and surgical systems. In such settings, surgeons often need to make high-stakes, time-sensitive, and complex decisions with limited or uncertain information. AI has great potential to augment the pre-operative, intra-operative, and post-operative phases of trauma care. Despite the expeditious advancement of AI, many surgeons lack a foundational understanding of AI terminology, its processes, and potential applications in clinical practice. This narrative review aims to educate general surgeons about the basics of AI, highlight its applications in thoraco-abdominal trauma, and discuss the implications of incorporating its use into the Australian health care system. This review found that studies of AI in trauma care have predominantly focused on machine learning and deep learning applied to diagnostics, risk prediction, and decision-making. Other subfields of AI include natural language processing and computer vision. While AI tools have many potential applications in trauma care, current clinical use is limited. Future prospective, locally validated research is required prior to incorporating AI into clinical practice.

Moving towards the use of artificial intelligence in pain management

BACKGROUND AND OBJECTIVE

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

DATABASES AND DATA TREATMENT

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

RESULTS

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

CONCLUSION

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

SIGNIFICANCE

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Establishing a novel score system and using it to assess and compare the quality of ChatGPT-4 consultation with physician consultation for obstetrics and gynecology: A pilot study

OBJECTIVES

In the current study, we aimed to establish a quantified scoring system for evaluating consultation quality. Subsequently, using the score system to assess the quality of ChatGPT-4 consultations, we compared them with physician consultations when presented with the same clinical cases from obstetrics and gynecology.

METHODS

This study was conducted in the Women and Children's Hospital of Chongqing Medical University, a tertiary-care hospital with approximately 16 000-20 000 deliveries and 8500-12 000 gynecologic surgeries per year. The detailed data from obstetric and gynecologic medical records were analyzed by ChatGPT-4 and physicians; the consultation opinions were then generated respectively. All consultation opinions were graded by eight junior doctors using the novel score system; subsequently, the correlation, agreement, and comparison between the two types of consultation opinions were then evaluated.

RESULTS

A total of 100 medical records from obstetrics and 100 medical records from gynecology were randomly selected. Pearson correlation analysis suggested a noncorrelation or weak correlation between consultations from ChatGPT-4 and physicians. Bland-Altman plot showed an unacceptable agreement between the two types of consultation opinions. Paired t tests showed that the scores of physician consultations were significantly higher than those generated by ChatGPT-4 in both obstetric and gynecologic patients.

CONCLUSION

At present, ChatGPT-4 may not be a substitute for physicians in consultations for obstetric and gynecologic patients. Therefore, it is crucial to pay careful attention and conduct ongoing evaluations to ensure the quality of consultation opinions generated by ChatGPT-4.

Clinical Characteristics and Chemosensitivity in Germline TP53 Pathogenic Variant Cases Identified by Cancer Genomic Testing

BACKGROUND/AIM

The widespread implementation of cancer genomic profiling (CGP) has led to an increase in the detection of germline TP53 pathogenic variants (gTP53v) in patients who do not meet the classical Li-Fraumeni syndrome (LFS) criteria. The present study aimed to characterize the clinical features and treatment outcomes of gTP53v cases identified through routine CGP testing.

PATIENTS AND METHODS

We conducted a retrospective analysis of 43 patients with gTP53v identified through CGP testing between June 2019 and August 2024. Clinical characteristics, molecular features, and treatment outcomes were analyzed and compared with TP53 wild-type cases from the same database (n=6,515).

RESULTS

The median age at diagnosis was 38 years (range=1-83 years), with 58.1% of cases presenting with non-core LFS tumors. A genomic analysis revealed diverse variant types (missense: 32, frameshift: 8, and nonsense: 3) with variant allele frequencies ranging between 0.10 and 0.696. Among 37 patients who received first-line chemotherapy, the objective response rate was 62%, which was significantly higher than in TP53 wild-type cases (32%, p=0.02). Complete responses were observed in six patients and partial responses in 14.

CONCLUSION

The present results suggest that gTP53v carriers identified through CGP represent a broader clinical spectrum than classical LFS, while demonstrating potentially favorable treatment outcomes. These results challenge traditional paradigms and emphasize the need for individualized approaches to patient care, particularly in cases with atypical presentations requiring the careful interpretation of mosaicism, de novo mutations, and clonal hematopoiesis.

Systematic Review Comparing Open Versus Minimally Invasive Surgical Management of Intradural Extramedullary Tumours (IDEM)

Background/Objectives: Intradural extramedullary (IDEM) spinal tumours are relatively rare and predominantly benign. Gross total resection (GTR) has been demonstrated as an effective treatment, with increasing evidence supporting the use of minimally invasive techniques to achieve GTR. This study reviews the current surgical management options for IDEM tumours and their outcomes. Methods: A systematic literature search without meta-analysis was conducted by two independent reviewers in December 2024. The population of interest comprised patients who underwent surgical treatment for IDEM tumours. Outcomes assessed included the extent of resection, postoperative neurological function, and complications. Results: Fifty-seven articles met the inclusion criteria, providing data on 4695 IDEM cases, of which 3495 were managed through open surgery and 750 via minimally invasive surgery. The extent of resection was high, with a mean GTR > 90% across studies. Open laminectomy and unilateral minimally invasive hemilaminectomy were the most common surgical approaches. Complications, such as cerebrospinal fluid leaks, were less frequent following minimally invasive procedures vs. open surgery (11.1% vs. 14.3%). Minimally invasive surgery also led to improved postoperative functional outcomes (mean McCormick score change -1.30 vs. -0.64) and a lower recurrence rate (1.4% vs. 10.0%). Conclusions: Whilst open surgery yields acceptable rates of resection and neurological improvement, there is growing evidence that minimally invasive surgery can achieve comparable, if not superior, rates of resection with fewer complications, leading to lower costs and shorter hospital stays.

Data - Knowledge driven machine learning model for cancer pain medication decisions

BACKGROUND

Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledge to support drug decision-making in cancer pain management.

METHODS

Clinical records from the Xiangya Hospital information system and a specialized cancer pain platform were used to develop two machine learning models: one for patients newly experiencing pain and one for patients with inadequate pain control. A total of 10,317 clinical records were used for model training, and 1,000 external records were obtained from the Cancer Hospital of the Chinese Academy of Medical Sciences for validation. Model performance was evaluated based on accuracy, AUC, and brier score.

RESULTS

Decision Tree and Gradient Boosting algorithms were selected for the two models, achieving an average accuracy of 98.47% and 94.74%, respectively, with AUCs of 99.62% and 94.74%. External validation accuracy was 97.4% and 93.1%, respectively, with AUCs of 99.83% and 97.01%.

CONCLUSION

The models proposed in this study can serve as decision support tools for healthcare professionals, assisting physicians in making optimized medication decisions in the absence of pharmacists.

Assessing the accuracy of ChatGPT in interpreting blood gas analysis results ChatGPT-4 in blood gas analysis

BACKGROUND

Arterial blood gas (ABG) analysis is a critical component of patient management in intensive care units (ICUs), operating rooms, and general wards, providing essential information on acid-base balance, oxygenation, and metabolic status. Interpretation requires a high level of expertise, potentially leading to variability in accuracy. This study explores the feasibility and accuracy of ChatGPT-4, an AI-based model, in interpreting ABG results compared to experienced anesthesiologists.

METHODS

This prospective observational study, approved by the institutional ethics board, included 400 ABG samples from ICU patients, anonymized and assessed by ChatGPT-4. The model analyzed parameters including acid-base status, oxygenation, hemoglobin levels, and metabolic markers, and provided both diagnostic and treatment recommendations. Two anesthesiologists, trained in ABG interpretation, independently evaluated the model's predictions to determine accuracy in potential diagnoses and treatment.

RESULTS

ChatGPT-4 achieved high accuracy across most ABG parameters, with 100 % accuracy for pH, oxygenation, sodium, and chloride. Hemoglobin accuracy was 92.5 %, while bilirubin interpretation showed limitations at 72.5 %. In several cases, the model recommended unnecessary bicarbonate treatment, suggesting an area for improvement in clinical judgment for acid-base balance management. The model's overall performance was statistically significant across most parameters (p < 0.05).

DISCUSSION

ChatGPT-4 demonstrated potential as a supplementary tool for ABG interpretation in high-demand clinical settings, supporting rapid, reliable decision-making. However, the model's limitations in interpreting complex metabolic markers highlight the need for clinician oversight. Future refinements should focus on enhancing AI training for nuanced metabolic interpretation, particularly for markers like bilirubin, to ensure safe and effective application across diverse clinical contexts.

A Survey on the Prescribing Orientation Towards Complementary Therapies Among Oncologists in Italy: Symptoms and Unmet Patient Needs

BACKGROUND/AIM

A high percentage of cancer patients use complementary therapies (CM) during their disease journey. Several barriers for CM prescription still exist among oncologists. This study explored oncologists' attitudes toward prescribing CM with oral supplements or confirming prescriptions made by others.

MATERIALS AND METHODS

The study employed a mixed semi-quantitative and qualitative research strategy via a web-based platform interview as a preliminary step for a program of observational studies on the oncologist's prescriptions of oral supplements in cancer management, in Italy.

RESULTS

Out of 95 invited oncologists, 40 participated in the study, mainly working in a general hospital or a cancer center. The deep knowledge of guidelines on integrative medicine was generally poor. The symptoms driving oncologists to initiate discussions on CM with patients were fatigue, anorexia/poor appetite, weight loss, insomnia, distress, neuropathy, or pain. The presence of reliable data in the medical literature on prescribing CM was a significant factor in choosing a supplement.

CONCLUSION

This study reveals that oncologists' limited knowledge and lack of standardized guidelines hinder the prescription of CM, despite recognizing its potential benefits. CM discussions are primarily patient-driven, with prescriptions influenced by reliable scientific data and symptom management. Expanding integrative medicine services and research on CM efficacy could enhance oncologists' confidence, improve patient care, and address unmet needs in oncology.

Racial, ethnic, and sex bias in large language model opioid recommendations for pain management

ABSTRACT

Understanding how large language model (LLM) recommendations vary with patient race/ethnicity provides insight into how LLMs may counter or compound bias in opioid prescription. Forty real-world patient cases were sourced from the MIMIC-IV Note dataset with chief complaints of abdominal pain, back pain, headache, or musculoskeletal pain and amended to include all combinations of race/ethnicity and sex. Large language models were instructed to provide a subjective pain rating and comprehensive pain management recommendation. Univariate analyses were performed to evaluate the association between racial/ethnic group or sex and the specified outcome measures-subjective pain rating, opioid name, order, and dosage recommendations-suggested by 2 LLMs (GPT-4 and Gemini). Four hundred eighty real-world patient cases were provided to each LLM, and responses included pharmacologic and nonpharmacologic interventions. Tramadol was the most recommended weak opioid in 55.4% of cases, while oxycodone was the most frequently recommended strong opioid in 33.2% of cases. Relative to GPT-4, Gemini was more likely to rate a patient's pain as "severe" (OR: 0.57 95% CI: [0.54, 0.60]; P < 0.001), recommend strong opioids (OR: 2.05 95% CI: [1.59, 2.66]; P < 0.001), and recommend opioids later (OR: 1.41 95% CI: [1.22, 1.62]; P < 0.001). Race/ethnicity and sex did not influence LLM recommendations. This study suggests that LLMs do not preferentially recommend opioid treatment for one group over another. Given that prior research shows race-based disparities in pain perception and treatment by healthcare providers, LLMs may offer physicians a helpful tool to guide their pain management and ensure equitable treatment across patient groups.

Sugammadex Safely Reduces Total Intubation Time in the Intensive Care Unit Following Coronary Artery Bypass Grafting (CABG) at a Real-World Community Hospital

Background/Objectives: Early extubation is crucial for enhancing recovery from coronary artery bypass grafting (CABG). Residual neuromuscular blockade (NMB) effects can hinder early extubation, potentially leading to reintubation, lung infection, and prolonged post-anesthesia stay. Sugammadex, a modified gamma-cyclodextrin, reverses the non-depolarizing NMB effects of the steroidal muscle relaxants rocuronium and vecuronium. The American Society of Anesthesiologists recommends sugammadex administration when patients display a train-of-four (TOF) ratio of less than 0.9. Previous studies show that sugammadex decreases extubation times, reduces postoperative complications, and enhances patient comfort. Methods: This single-center, retrospective cohort study evaluated the efficacy of sugammadex in achieving extubation within six hours of intensive care unit (ICU) arrival post-CABG, defined as fast-track extubation (FTE). Results: Here, we report that although the total time of intubation in the ICU following CABG did not drop to the six-hour benchmark, it was substantially reduced by the administration of sugammadex in accordance with an FTE protocol. Furthermore, the risks of adverse events (e.g., anaphylaxis, heart failure) and postoperative complications (e.g., acidemia, hypoxemia, tachypnea) were unaltered. Conclusions: The use of sugammadex could, thus, reduce costs associated with prolonged intubation time and related complications without increasing morbidity or mortality.

Prodelphinidin B-2,3,3"-O-gallate via chemical oxidation of epigallocatechin-3-gallate shows high efficacy inhibiting triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer is a clinically aggressive malignancy with poorer outcomes versus other subtypes of breast cancer. Numerous reports have discussed the use of epigallocatechin-3-gallate (EGCG) against various types of cancer. However, the effectiveness of EGCG is limited by its high oxidation and instability. The Notch pathway is critical in breast cancer development and prognosis, and its inhibition is a potential treatment strategy.

RESULTS

In this study, we investigated the effects of prodelphinidin B-2,3,3''-O-gallate (named PB2,3,3''/OG or compound 2) via chemical oxidation of EGCG on cell viability and the Notch1 signaling pathway in breast cancer cells. We found that compound 2 showed significant cytotoxicity against triple-negative breast cancer cells, with the half maximal inhibitory concentration (IC50) values ranging 20-50 µM. In MDA-MB453 cells, compound 2 inhibited proliferation, clone formation, and the expression of proteins involved in the Notch1 signaling pathway. Furthermore, compound 2 induced cell cycle arrest and apoptosis. Consistent with the results of in-vitro experiments, treatment with compound 2 significantly reduced tumor growth. Mechanistically, compound 2 directly bound to Notch1 with high binding affinity (dissociation constant: KD=4.616 × 10- 6 M).

CONCLUSION

Our finding suggested that compound 2 may be a promising agent for the development of novel anti-cancer therapy options.

Success rates of video vs. direct laryngoscopy for endotracheal intubation in anesthesiology residents: a study protocol for a randomized controlled trial (JuniorDoc-VL-Trial)

BACKGROUND

Tracheal intubation is a core skill in airway management for anesthesiologists as well as for other medical professionals involved in advanced airway procedures. Traditionally, tracheal intubation in hospitals has been performed using a Macintosh blade for direct laryngoscopy (DL). However, recent literature increasingly supports the potential benefits of routine video laryngoscopy (VL). The aim of this study was to assess whether primary training in hyperangulated VL improves the first-pass success rate of tracheal intubation among first-year anesthesiology residents, compared to conventional DL training, in the operating room.

METHODS

The JuniorDoc-VL Trial is a randomized, controlled, patient-blinded clinical trial of novice anesthesiology residents trained in DL and VL. Thirty residents will be randomly assigned to either the intervention group (VL group) or the control group (DL group) with a 1:1 allocation. The first-pass-success (FPS) rates (primary endpoint) and complication rates (secondary endpoint) will be compared between groups.

DISCUSSION

We hypothesize that the primary use of hyperangulated video laryngoscopy (VL) in the experimental group will increase first-pass-success rates among inexperienced residents and reduce complication rates associated with advanced airway management in a mixed patient population. This study may provide an opportunity to develop strategies that allow physicians not routinely involved in anesthesia to effectively learn and maintain their skills in tracheal intubation.

TRIAL REGISTRATION

ClinicalTrials.gov Registry (NCT06360328). Registered on 09.04.2024.

Leukaemia-associated priapism in children (LAPC): reviewing clinical outcomes and management strategies

Background

Priapism is a prolonged penile erection for more than 4 hours unrelated to sexual stimulation. Rarely, it is the first clinical sign of an underlying haematological malignancy. A similar presentation is noted in childhood leukaemias. Although rare, it is known to occur and, if not managed early, can have poor long-term outcomes in terms of erectile function and psychosexual growth. We present a scoping review of leukaemia-associated priapism in children (LAPC).

Methodology

We researched literature using PubMed, Google Scholar, Embase, Scopus and Cochrane databases from January 1990 to 2024. Applicable search limiters were applied, and grey literature was excluded.

Results

A total of 31 articles were finally included in the review, from which 51 cases of LAPC were isolated and studied. The average age was 11.5 years, with chronic myeloid leukaemia (CML) being the most common malignancy (68.9%), and more than 71% of cases of CML with priapism were detected in the chronic phase. Twenty cases (39.2%) were managed with corporal lavage and sympathomimetic injections at the initial onset, with the rest managed with cytoreductive measures initially. Follow-up data revealed the death of three children, whereas, of those that survived, fourteen had preserved erectile functions after a variable period of time.

Conclusion

Priapism in children warrants a thorough physical examination focusing on organomegaly and a complete hemogram. Initial management should be two-pronged with a priapism-directed corporal-lavage approach and cytoreductive measures for better long-term outcomes.

Advantages and limitations of large language models for antibiotic prescribing and antimicrobial stewardship

Antibiotic prescribing requires balancing optimal treatment for patients with reducing antimicrobial resistance. There is a lack of standardization in research on using large language models (LLMs) for supporting antibiotic prescribing, necessitating more efforts to identify biases and misinformation in their outputs. Educating future medical professionals on these aspects is crucial for ensuring the proper use of LLMs for supporting antibiotic prescribing, providing a deeper understanding of their strengths and limitations.

Mitophagy in Neurodegenerative Diseases: Mechanisms of Action and the Advances of Drug Discovery

Neurodegenerative diseases (NDDs), such as Parkinson's disease (PD) and Alzheimer's disease (AD), are devastating brain diseases and are incurable at the moment. Increasing evidence indicates that NDDs are associated with mitochondrial dysfunction. Mitophagy removes defective or redundant mitochondria to maintain cell homeostasis, whereas deficient mitophagy accelerates the accumulation of damaged mitochondria to mediate the pathologies of NDDs. Therefore, targeting mitophagy has become a valuable therapeutic pathway for the treatment of NDDs. Several mitophagy modulators have been shown to ameliorate neurodegeneration in PD and AD. However, it remains to be further investigated for other NDDs. Here, we describe the mechanism and key signaling pathway of mitophagy and summarize the roles of defective mitophagy on the pathogenesis of NDDs. Further, we underline the development advances of mitophagy modulators for PD and AD therapy, discuss the therapeutic challenges and limitations of the existing modulators, and provide guidelines for mitophagy mechanism exploration and drug design.

Effects of nirmatrelvir/ritonavir (Paxlovid) on the nervous system: analysis on adverse events released by FDA

BACKGROUND

Nirmatrelvir/ritonavir, commonly known as Paxlovid, is one of the main drugs used to treat COVID-19. Neurological disorders are among the adverse drug reactions (ADRs) linked to Paxlovid, yet comprehensive data-mining studies based on real-world neurological adverse events induced by Paxlovid are lacking.

METHODS

It is an observational study, to reduce the risk of bias affected by COVID-19 disease, our study included only patients with COVID-19 disease. In this case, disproportionate analysis is performed using the Report Odds Ratio (ROR) and its 95% Confidence Interval (CI).

RESULTS

We screened and compared all medications associated with COVID-19 (N = 439) and found that 22 of these were linked to neurological adverse reactions. Paxlovid was associated with a threefold greater number of neurological adverse events compared to all other drugs combined (N = 11,792), with a strong signal value (ROR = 2.27).

CONCLUSIONS

Compared to all other COVID-19-related drugs, Paxlovid has the highest number and stronger signal value for neurologic-related adverse reactions. Clinicians should pay special attention to female patients taking Paxlovid within the first 30 days, monitoring for symptoms such as dysgeusia, ageusia, headache, and anosmia. In addition, headache and anosmia are not uncommon occurrences as mentioned in the instructions and should be noted.

A Meta-Analysis of the Impact of Intranasal Dexmedetomidine on Emergence Delirium and Agitation in Children and Adolescents Undergoing Tonsillectomy and/or Adenoidectomy

Background: Tonsillectomy and adenoidectomy are two common pediatric operations that are frequently associated with postoperative problems like emergence agitation (EA) and emergence delirium (ED). Intranasal dexmedetomidine, which has anxiolytic and sedative qualities with low respiratory effects, is becoming increasingly popular as a premedication in pediatric patients. However, there is limited evidence on its efficacy in tonsillectomy and/or adenoidectomy. This original research is a meta-analysis examining the impact of intranasal dexmedetomidine on EA, ED, and other perioperative outcomes in children having these procedures. Methods: A thorough search of the PubMed, Scopus, Web of Science, and Cochrane Library databases was performed for randomized controlled trials (RCTs) published by January 2025 of select studies on children undergoing tonsillectomy and/or adenoidectomy. The intervention was intranasal dexmedetomidine (1-2 µg/kg), whereas the comparator was placebo/no intervention. Results: Four RCTs with 669 children met our inclusion criteria. Intranasal dexmedetomidine substantially decreased the incidence of EA (RR = 0.39, 95% CI: 0.16 to 0.92, p = 0.03) and ED (RR = 0.45, 95% CI: 0.24 to 0.84, p = 0.01), despite significant heterogeneity. Pediatric Anesthesia Emergency Delirium (PAED) scores were also considerably lower in the dexmedetomidine group (MD = -2.11, 95% CI interval: -3.77 to -0.44, p = 0.01). We found significant changes in extubation time (p = 0.91) or PACU discharge time (p = 0.53). Conclusions: Intranasal dexmedetomidine may reduce the occurrence of EA and ED, while also lowering PAED scores in children undergoing tonsillectomy and/or adenoidectomy. And although it has demonstrated safety with few side effects, more research is needed to validate its impact on other perioperative outcomes and enhanced dosing regimens.

Prognostic Biomarkers in Breast Cancer via Multi-Omics Clustering Analysis

Breast cancer (BC) is a highly heterogeneous disease with diverse molecular subtypes, which complicates prognosis and treatment. In this study, we performed a multi-omics clustering analysis using the Cancer Integration via MultIkernel LeaRning (CIMLR) method on a large BC dataset from The Cancer Genome Atlas (TCGA) to identify key prognostic biomarkers. We identified three genes-LMO1, PRAME, and RSPO2-that were significantly associated with poor prognosis in both the TCGA dataset and an additional dataset comprising 146 metastatic BC patients. Patients' stratification based on the expression of these three genes revealed distinct subtypes with markedly different overall survival (OS) outcomes. Further validation using almost 2000 BC patients' data from the METABRIC dataset and RNA sequencing data from therapy-resistant cell lines confirmed the upregulation of LMO1 and PRAME, respectively, in patients with worse prognosis and in resistant cells, also suggesting their potential role in drug resistance. Our findings highlight LMO1 and PRAME as potential biomarkers for identifying high-risk BC patients and informing targeted treatment strategies. This study provides valuable insights into the multi-omics landscape of BC and underscores the importance of personalized therapeutic approaches based on molecular profiles.

Extracellular vesicle-mediated chemoresistance in breast cancer: focus on miRNA cargo

The role of extracellular vesicles (EVs) in mediating chemoresistance has gained significant attention due to their ability to transfer bioactive molecules between drug-resistant and drug-sensitive cells. In particular, they have been demonstrated to play an active part in breast cancer chemoresistance by the horizontal transfer of genetic and protein material. This review highlights the role of EVs, particularly their miRNA cargo, in driving drug resistance in breast cancer. EVs derived from chemoresistant cells carry miRNAs and lncRNAs, which are known to modulate gene networks involved in cell proliferation and survival. These cargo molecules suppress apoptosis by targeting pro-apoptotic genes like PTEN and BIM, promote epithelial-mesenchymal transition (EMT) through the regulation of pathways such as TGF-β and Wnt/b-catenin, and contribute to tumor growth and resistance by enhancing angiogenesis and modulating the tumor microenvironment. Beyond RNA-mediated effects, EVs also transfer functional proteins, including P-glycoprotein and Hsp70, which impact cellular metabolism and survival pathways. Our findings underscore the significance of EVs in breast cancer chemoresistance, suggesting their potential involvement as possible prognostic factors to predict therapy response and as therapeutic targets in combination with usual therapy.

Green Tea: Current Knowledge and Issues

Green tea possesses antioxidant, anti-inflammatory, anticancer, and antimicrobial activities, reduces body weight, and slows down aging. These effects are primarily attributed to catechins contained in green tea leaves, particularly epigallocatechin-3-gallate. However, in humans, the realization of green tea's beneficial effects is limited. In order to summarize and critically analyze the available scientific information about green tea's health benefits and issues related to its use, we conducted an in-depth literature review in scientific databases. A number of in vitro studies reported that green tea catechins modulate various signaling pathways in cells, which is thought to underlie their beneficial effects. However, data on the effects of catechins in humans are scarce, which is partly due to their low stability and oral bioavailability. Furthermore, catechins may also participate in pharmacokinetic interactions when co-administered with certain drugs such as anticancer agents, drugs for cardiovascular diseases, immunosuppressors, etc. As a result, adverse drug reactions or therapy failure may occur. In conclusion, over the years, various approaches have been investigated to optimize catechin intake and to achieve beneficial effects in humans, but to date, the use of catechins for prophylaxis or disease treatment remains limited. Therefore, future studies regarding the possibilities of catechins administration are needed.

Pediatric Anesthesia: The Essential Value of a Well-Supported Clinic

This Special Issue on pediatric anesthesia aims to showcase advanced research and expert perspectives that emphasize the crucial role of this specialty [...].

Change in the Order of Activation of Lower Limb Muscles Relative to Spinal Extensors During the Janda Test and Change in Postural Balance in Patients with LBP After Muscle Energy Techniques

Background: The aim of this study was to investigate change in the order activation of leg muscles in relation to the spinal extensors during the Janda test and change postural balance in patients with low back pain (LBP) after muscle energy techniques (METs). Methods: The study included fifteen men (mean age 41.9 years) working on an assembly line. The activity and recruitment of the following muscles: biceps femoris (BF), gluteus maximus (GM) and erector spinae (ES) were measured with the use of surface electromyography (sEMG) during the prone hip extension (PHE) test. Pain levels and postural balance were analysed. Results: MET translated into a reduction in muscle activation time in both the left and right side of the body (p < 0.001). The change was observed in the following muscles: BF (p = 0.003) and GM (p = 0.004). A reduction in pain was obtained post application of the MET therapy (p < 0.001). It was observed that after the therapy, along with the later activation time of the GM muscle, the range of motion of the COP along the x-axis increased (p = 0.0368). Increased activation time of the RES (p = 0.0411) and the LES (p = 0.0350) muscles influenced a decrease in the COP range of motion along the x-axis. Conclusions: The use of MET in people with LBP improves the sequence of activation of the ES, GM and BF muscles, affects postural balance, allows for the restoration of muscle balance in the lumbar spine and lower limbs, and reduces pain.

Reliability, Accuracy, and Comprehensibility of AI-Based Responses to Common Patient Questions Regarding Spinal Cord Stimulation

Background: Although spinal cord stimulation (SCS) is an effective treatment for managing chronic pain, many patients have understandable questions and concerns regarding this therapy. Artificial intelligence (AI) has shown promise in delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT's responses to common patient inquiries about SCS. Methods: Thirteen commonly asked questions regarding SCS were selected based on the authors' clinical experience managing chronic pain patients and a targeted review of patient education materials and relevant medical literature. The questions were prioritized based on their frequency in patient consultations, relevance to decision-making about SCS, and the complexity of the information typically required to comprehensively address the questions. These questions spanned three domains: pre-procedural, intra-procedural, and post-procedural concerns. Responses were generated using GPT-4.0 with the prompt "If you were a physician, how would you answer a patient asking…". Responses were independently assessed by 10 pain physicians and two non-healthcare professionals using a Likert scale for reliability (1-6 points), accuracy (1-3 points), and comprehensibility (1-3 points). Results: ChatGPT's responses demonstrated strong reliability (5.1 ± 0.7) and comprehensibility (2.8 ± 0.2), with 92% and 98% of responses, respectively, meeting or exceeding our predefined thresholds. Accuracy was 2.7 ± 0.3, with 95% of responses rated sufficiently accurate. General queries, such as "What is spinal cord stimulation?" and "What are the risks and benefits?", received higher scores compared to technical questions like "What are the different types of waveforms used in SCS?". Conclusions: ChatGPT can be implemented as a supplementary tool for patient education, particularly in addressing general and procedural queries about SCS. However, the AI's performance was less robust in addressing highly technical or nuanced questions.

An ultrasound-guided modified iliac fascia and sacral plexus block application in a critically ill patient undergoing artificial femoral head replacement surgery: a case report

BACKGROUND

Hip fractures have become a significant clinical concern on a global scale in recent years. The burgeoning aging population has exacerbated this issue, leading to a rise in the number of hip fracture cases coupled with concomitant geriatric ailments. Therefore, it poses a huge challenge to anesthesiologists with the increasing number of critically ill patients who are not suitable for general anesthesia and intrathecal anesthesia. Ultrasound-guided nerve blocks combined with sedation have not previously been documented in critically ill patients.

CASE PRESENTATION

We administered an ultrasound-guided modified iliac fascia block combined with sacral plexus block to an 88-year-old male patient with a left femoral fracture suffering from severe multi-systemic diseases. Concurrently, the patient received intravenous anesthesia via propofol and dexmedetomidine, with monitoring conducted through bispectral index. Subsequently, the surgery was completed smoothly and the patient demonstrated accelerated postoperative recovery without significant discomfort and a successful discharge.

CONCLUSIONS

This innovative approach significantly contributed to achieving successful surgical anesthesia and postoperative analgesia, thereby facilitating early recovery and discharge from the hospital. We propose that ultrasound-guided nerve blocks with sedation show promise for critically ill patients.

Molecular Markers of Occult Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

The accurate diagnosis of regional lymph node metastasis is critical for guiding treatment decisions in head and neck cancer patients. Despite advances in imaging techniques, detecting nodal metastasis using radiology remains challenging, leading to potential undertreatment or overtreatment. This review aims to identify molecular markers associated with occult metastasis in head and neck squamous cell carcinoma (HNSCC) patients. We divided the results by subsite for markers: lymph node analysis (microRNAs, myosin-5a (MYO5A), ring finger protein 145 (RNF145), F-box only protein 32 (FBXO32), CTONG2002744, cytokeratin 14 (CK14), eukaryotic initiation factor 4E (eIF4E), desmoglein-3 (DSG3), microsatellite D9S171, squamous cell carcinoma antigen, cytokeratin, tumor budding score, human papillomavirus-DNA (HPV-DNA), tumor infiltrating lymphocytes, sentinel lymph node analysis techniques, single fiber reflectance spectroscopy, radiological techniques), tumor tissue analysis (activin A, carcinoma-associated fibroblasts, cyclins, β-catenin, histopathology, genetic amplifications, DNA methylation, ecotropic viral integration site 1, CC-chemokine receptor 7, melanoma associated-A antigens, vascular endothelial growth factor-C (VEGF-C), panitumumab, epidermal growth factor receptor (EGFR), cornulin, total protein analysis, CD133, NANOG homeobox, neurogenic locus notch homolog protein 1 (NOTCH1), metastasis-associated protein 1, 14-3-3-zeta, E-cadherin, focal adhesion kinase, p-epithelial-mesenchymal transition (EMT), small proline rich protein 1B (SPRR1B), transcription factor NKX3-1, DNA copy number aberrations, microfibril-associated protein 5 (MFAP5), troponin C1, slow skeletal and cardiac type (TNNC1), matrix Gla protein (MGP), fibroblast growth factor binding protein 1 (FBFBP1), F-box protein 32 (FBXO32), fatty acid binding protein 5, B cell-specific Moloney murine leukemia virus integration site 1, podoplanin, p53, Bcl-2, epidermal growth factor receptor (EGFR), Ki67, cyclin D1, cox-2, semaphorin-3F, neuropilin-2, histologic features, cellular dissociation grade, prospero homeobox protein 1, radiologic features, Ki-67, poly (ADP-ribose) polymerase (PARP), Bcl-2 associated agonist of cell death (BAD), caspase-9, vascular endothelial growth factor A (VEGF-A), HPV, p16, methylation status of long interspersed element 1 (LINE-1) and Alu elements, mesenchymal-epithelial transition (MET), gene expression analyses, molecular subtypes) and blood markers (standard blood analysis indexes and ratios, circulating tumor cells, HPV-DNA, CD-31, bone marrow analysis). Several promising markers were identified, including miR-205, desmoglein 3 (DSG3), pan-cytokeratin (CK) AE1/AE3, HPV-16, activin-A, cyclin D1, E-cadherin, and neural progenitor lineage (NPL) that demonstrated effectiveness across multiple studies. Future research should focus on exploring combination scoring systems to improve diagnostic precision and optimize treatment selection in HNSCC patients.

AI detectors are poor western blot classifiers: a study of accuracy and predictive values

The recent rise of generative artificial intelligence (AI) capable of creating scientific images presents a challenge in the fight against academic fraud. This study evaluates the efficacy of three free web-based AI detectors in identifying AI-generated images of western blots, which is a very common technique in biology. We tested these detectors on AI-generated western blot images (n = 48, created using ChatGPT 4) and on authentic western blots (n = 48, from articles published before the rise of generative AI). Each detector returned a very different sensitivity (Is It AI?: 0.9583; Hive Moderation: 0.1875; and Illuminarty: 0.7083) and specificity (Is It AI?: 0.5417; Hive Moderation: 0.8750; and Illuminarty: 0.4167), and the predicted positive predictive value (PPV) for each was low. This suggests significant challenges in confidently determining image authenticity based solely on the current free AI detectors. Reducing the size of western blots reduced the sensitivity, increased the specificity, and did not markedly affect the accuracy of the three detectors, and only slightly improved the PPV of one detector (Is It AI?). These findings highlight the risks of relying on generic, freely available detectors that lack sufficient reliability, and demonstrate the urgent need for more robust detectors that are specifically trained on scientific contents such as western blot images.

Correlation Between Skeletal Muscle Mass and Different Pathological Types of Colorectal Polyp in Chinese Asymptomatic Population

Background

Low relative muscle mass was identified to be related to ascending risk of pre-cancerous polyps (adenoma) in recent cohort study. Our study aimed to dig out the correlation between muscle mass and different pathological types of colorectal polyps in Chinese asymptomatic population.

Methods

In all, 5923 adults were included. The effects of low skeletal muscle mass index (SMI) on colorectal polyp occurrence, including different pathological types, and the effects modification of age and BMI were analyzed using univariate and multivariate logistic regression.

Results

Lower SMI was connected with the lower occurrence of colorectal polyp (OR: 0.810, 95% CI: 0.683~0.960, p=0.015). Considering different pathological types of colorectal polyps, lower SMI was associated with lower occurrence of inflammatory polyp (OR: 0.633, 95% CI: 0.434~0.898, p=0.013), rather than conventional adenoma and serrated polyp (all p>0.05). Besides, SMI was positively related to the occurrence of 2 pathological types of colorectal polyp in males: inflammatory polyp (OR: 1.237, 95% CI: 1.058~1.444, p=0.007) and serrated polyp (OR: 1.288, 95% CI: 1.143~1.456, p<0.001). The interaction effect of BMI and SMI on occurrence of inflammatory polyp after adjusting age and smoking status was significant (p=0.015). For individuals with low SMI (compared with the normal SMI group), the incidence of inflammatory polyp was reduced from 8.95% to 3.50% in the low BMI quartile (Q1) in the adjusted model (OR of 0.332, 95% CI: 0.005-0.061, p<0.001). It was noticeable for males rather than females that individuals with colorectal polyps had higher levels of SMI (p=0.003). In addition, individuals with inflammatory polyps as well as serrated polyps possessed higher levels of SMI in males (all p<0.05).

Conclusion

Generally, especially in Chinese asymptomatic males, low SMI kept independent effect on the presence of inflammatory polyp and serrated polyp, rather than conventional adenoma.

Perceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study

BACKGROUND

With the rapid development of artificial intelligence technologies, there is a growing interest in the potential use of artificial intelligence-based tools like ChatGPT in medical education. However, there is limited research on the initial perceptions and experiences of faculty and students with ChatGPT, particularly in Saudi Arabia.

OBJECTIVE

This study aimed to explore the earliest knowledge, perceived benefits, concerns, and limitations of using ChatGPT in medical education among faculty and students at a leading Saudi Arabian university.

METHODS

A qualitative exploratory study was conducted in April 2023, involving focused meetings with medical faculty and students with varying levels of ChatGPT experience. A thematic analysis was used to identify key themes and subthemes emerging from the discussions.

RESULTS

Participants demonstrated good knowledge of ChatGPT and its functions. The main themes were perceptions of ChatGPT use, potential benefits, and concerns about ChatGPT in research and medical education. The perceived benefits included collecting and summarizing information and saving time and effort. However, concerns and limitations centered around the potential lack of critical thinking in the information provided, the ambiguity of references, limitations of access, trust in the output of ChatGPT, and ethical concerns.

CONCLUSIONS

This study provides valuable insights into the perceptions and experiences of medical faculty and students regarding the use of newly introduced large language models like ChatGPT in medical education. While the benefits of ChatGPT were recognized, participants also expressed concerns and limitations requiring further studies for effective integration into medical education, exploring the impact of ChatGPT on learning outcomes, student and faculty satisfaction, and the development of critical thinking skills.

Commonly used interventional procedures for non-cancer chronic spine pain: a clinical practice guideline

CLINICAL QUESTION

What is the comparative effectiveness and safety of commonly used interventional procedures (such as spinal injections and ablation procedures) for chronic axial and radicular spine pain that is not associated with cancer or inflammatory arthropathy?

CURRENT PRACTICE

Chronic spine pain is a common, potentially disabling complaint, for which clinicians often administer interventional procedures. However, clinical practice guidelines provide inconsistent recommendations for their use.

RECOMMENDATIONS

For people living with chronic axial spine pain (≥3 months), the guideline panel issued strong recommendations against: joint radiofrequency ablation with or without joint targeted injection of local anaesthetic plus steroid; epidural injection of local anaesthetic, steroids, or their combination; joint-targeted injection of local anaesthetic, steroids, or their combination; and intramuscular injection of local anaesthetic with or without steroids. For people living with chronic radicular spine pain (≥3 months), the guideline panel issued strong recommendations against: dorsal root ganglion radiofrequency with or without epidural injection of local anaesthetic or local anaesthetic plus steroids; and epidural injection of local anaesthetic, steroids, or their combination.

HOW THIS GUIDELINE WAS CREATED

An international guideline development panel including four people living with chronic spine pain, 10 clinicians with experience managing chronic spine pain, and eight methodologists, produced these recommendations in adherence with standards for trustworthy guidelines using the GRADE approach. The MAGIC Evidence Ecosystem Foundation provided methodological support. The guideline panel applied an individual patient perspective when formulating recommendations.

THE EVIDENCE

These recommendations are informed by a linked systematic review and network meta-analysis of randomised trials and a systematic review of observational studies, summarising the current body of evidence for benefits and harms of common interventional procedures for axial and radicular, chronic, non-cancer spine pain. Specifically, injection of local anaesthetic, steroids, or their combination into the cervical or lumbar facet joint or sacroiliac joint; epidural injections of local anaesthetic, steroids, or their combination; radiofrequency of dorsal root ganglion; radiofrequency denervation of cervical or lumbar facet joints or the sacroiliac joint; and paravertebral intramuscular injections of local anaesthetic, steroids, or their combination.

UNDERSTANDING THE RECOMMENDATIONS

These recommendations apply to people living with chronic spine pain (≥3 months duration) that is not associated with cancer or inflammatory arthropathy and do not apply to the management of acute spine pain. Further research is warranted and may alter recommendations in the future: in particular, whether there are differences in treatment effects based on subtypes of chronic spine pain, establishing the effectiveness of interventional procedures currently supported by low or very low certainty evidence, and effects on poorly reported patient-important outcomes (such as opioid use, return to work, and sleep quality).