A study to determine the effect of nano-selenium and thymoquinone on the Nrf2 gene expression in Alzheimer's disease

INTRODUCTION

Alzheimer's disease is a developing public health concern in aging communities that affects a sizable section of the global population. The risk of Alzheimer's disease increases with age; it affects one-third of males and two-thirds of women. This research attempts to assess the effect of nano-selenium and thymoquinone on Nrf2 gene expression levels in Alzheimer's disease (AD).

METHODS

There were five identical groups of 50 albino male rats: a control group that was healthy; an AD positive control group; an AD group that received nano-selenium (5 mg/kg); an AD group that received thymoquinone (2 mg/kg); and an AD group that received both. The duration of treatment was 4 weeks. The levels of Nrf2 in brain tissues were evaluated using real-time PCR.

RESULTS

Nrf2 mean expression levels in the nano-selenium-treated rats, the thymoquinone-treated rats, and the rats that were given both treatments all increased significantly compared to AD rats with no treatment.

CONCLUSIONS

This study showed that nano-selenium and thymoquinone elevated Nrf2 gene expression levels in AD.

Optimal doses of intranasal esketamine plus dexmedetomidine for sedating toddlers during transthoracic echocardiography: a prospective, double-blind, randomized trial

INTRODUCTION

Esketamine has unique advantages in combination with dexmedetomidine for sedation in young children, owing to its sympathetic activity and mild respiratory depression. However, the optimal dose is yet to be determined. In this study, we compared the different doses of intranasal esketamine combined with dexmedetomidine for sedation during transthoracic echocardiography in toddlers.

PATIENTS AND METHODS

A total of 121 eligible children aged 13 years, who were scheduled for transthoracic echocardiography were randomized into three groups. They were treated with intranasal dexmedetomidine 1 mcg.kg-1 + esketamine 0.5 mg.kg-1 (group S1), dexmedetomidine 1 mcg.kg-1 + esketamine 1 mg.kg-1 (group S2), or dexmedetomidine 1 mcg.kg-1 + esketamine 1.5 mg.kg-1 (group S3). The primary outcome was the success rate of sedation, other outcomes included HR, SpO2, onset time, wake-up time, and adverse effects.

RESULTS

The success rate of sedation was significantly higher in groups S2 (85.4%) and S3 (87.5%) than ingroup S1 (60%) (p = 0.004). The baseline HR and SpO2 did not differ between the groups at the corresponding time points following drug administration. The onset time and duration of sedation in group S1 were significantly longer than those in groups S2 and S3 (p = 0.000). However, there were no differences in the wake-up time or adverse effects among the three groups.

CONCLUSIONS

Intranasal administration of 1 mg.kg-1 esketamine combined with 1 mcg.kg-1 dexmedetomidine provided satisfactory sedation in young children undergoing transthoracic echocardiography. This sedative approach offers a rapid onset of awakening with few side effects.

CLINICAL TRIAL REGISTRATION NUMBER

ChiCTR2200060976, 2022/06/14 (trail from August 2022 to January 2023).

Exploring experiences and needs among children with cancer undergoing peripherally inserted central catheter insertion: A qualitative study

Objective

This study aimed to explore the experiences of children with cancer undergoing peripherally inserted central catheter (PICC) insertion.

Methods

This study employed a descriptive qualitative approach. A total of 20 children undergoing PICC insertion were enrolled through purposive sampling. Semi-structured interviews, supplemented by draw-and-tell techniques, were conducted to collect data. Thematic analysis method was employed to analyze the interview data.

Results

Four themes and 11 subthemes were identified regarding the experience of peripherally inserted central catheter insertion in pediatric cancer patients, including (1) uncertainty (unknown procedure and lack of confidence); (2) high sensitivity (vulnerable to environmental influences and care what others think); (3) psychophysical changes (stress response, physical discomfort, disruption of daily routines, and accepted with pleasure); and (4) multidimensional needs (information, comfort, and self-actualization needs).

Conclusions

The findings underscore the importance of gaining a deeper understanding of the symptoms and needs of pediatric cancer patients undergoing PICC insertion. By appreciating and honoring children's voices, we can effectively cater to their distinct worries and guarantee they get the care and consideration they merit.

Discovery of novel genipin derivatives as highly selective ferroptosis inducers for the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is limited in treatment options due to the absence of three receptors, and evidence suggests that TNBC is sensitive to ferroptosis. In this study, a series of genipin derivatives were synthesized through a hybridization strategy that integrated the structures of RSL3 and ML162. Among these derivatives, compound B23 demonstrated remarkable activity against the MDA-MB-231 cell line with an IC50 value of 40 nM, significantly outperforming genipin, RSL3 and ML162. Furthermore, B23 exhibited high selectivity for ferroptosis with a selectivity ratio of up to 108-fold. Further studies revealed that B23 induces ferroptosis by affecting the expression of ferroptosis-related proteins ACSL4, GPX4, and FTH1, thereby disrupting intracellular iron homeostasis and the GSH/GPX4 antioxidant defense system, ultimately leading to the accumulation of lipid peroxidation (LPO). Animal model demonstrated that B23 exhibited potent tumor suppression in an MDA-MB-231 xenograft model, achieving a tumor inhibition rate of 78.46 % at a dose of 4 mg/kg, without observable toxic side effects. In conclusion, B23 represents a promising ferroptosis inducer for the treatment of TNBC and warrants further investigation.

Evaluating the impact of explainable AI on clinicians' decision-making: A study on ICU length of stay prediction

BACKGROUND

Explainable Artificial Intelligence (XAI) is increasingly vital in healthcare, where clinicians need to understand and trust AI-generated recommendations. However, the impact of AI model explanations on clinical decision-making remains insufficiently explored.

OBJECTIVES

To evaluate how AI model explanations influence clinicians' mental models, trust, and satisfaction regarding machine learning-based predictions of Intensive Care Unit (ICU) Length of Stay (LOS).

METHODS

This retrospective mixed-methods study analyzed electronic health record data from 8,579 patients admitted to a surgical ICU in South Korea between 2019 and 2022. Seven machine learning models were developed and evaluated to predict ICU LOS at 2-hour intervals during the initial 12 hours post-admission. The Random Forest (RF) model in the 10- to 12-hour window, with an AUROC of 0.903, was selected for explanation using SHapley Additive exPlanations. Fifteen ICU clinicians assessed four distinct types of explanations ('Why', 'Why not', 'How to', and 'What if') via web-based experiments, surveys, and interviews.

RESULTS

Clinicians' feature selections aligned more closely with the RF model after explanations, as demonstrated by an increase in Spearman correlation from -0.147 (p = 0.275) to 0.868 (p < 0.001). The average trust score improved from 2.8 to 3.9. The average satisfaction scores for the 'Why', 'Why not', 'How to', and 'What if' explanations were 3.3, 3.8, 3.6, and 4.1, respectively.

CONCLUSION

AI model explanations notably enhanced clinicians' understanding and trust in AI-generated ICU LOS predictions, although complete alignment with their mental models was not achieved. Further refinement of AI model explanations is needed to support better clinician-AI collaboration and its integration into clinical practice.

Sensory neuron sodium channels as pain targets; from cocaine to Journavx (VX-548, suzetrigine)

Voltage-gated sodium channels underpin electrical signaling in sensory neurons. Their activity is an essential element in the vast majority of pain conditions, making them significant drug targets. Sensory neuron sodium channels play roles not only in afferent signaling but also in a range of efferent regulatory mechanisms. Side effects through actions on other cell types and efferent signaling are thus important issues to address during analgesic drug development. As an example, the human genetic evidence for NaV1.7 as an ideal pain target contrasts with the side effects of NaV1.7 antagonists. In this review, we describe the history and progress toward the development of useful analgesic drugs and the renewed focus on NaV1.8 as a key target in pain treatment. NaV1.8 antagonists alone or in combination with other analgesics are likely to provide new opportunities for pain relief for the vast number of people (about 33% of the population) impacted by chronic pain, particularly present in aging populations.

Preclinical evidence construction for epigallocatechin-3-gallate against non-alcoholic fatty liver disease: a meta-analysis and machine learning study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant health concern worldwide, exhibiting an increasing incidence that necessitates immediate intervention. Epigallocatechin-3-gallate (EGCG) has shown significant pharmacological benefits for liver diseases, including NAFLD. However, its efficacy in this context has not been systematically evaluated.

PURPOSE

This meta-analysis aimed to consolidate preclinical evidence on the effectiveness and mechanisms of EGCG in treating NAFLD.

METHODS

We conducted a comprehensive literature search for preclinical studies from the inception of each database to April 2024, including Excerpta Medica Database, PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang, and China Science and Technology Journal Database. These studies were manually screened based on predefined criteria. Data extraction was followed by pooled effect size calculations using Stata 16.0. A machine learning approach was also utilized to examine the temporal relationships among variables.

RESULTS

Seventeen studies, involving 293 animals, were analyzed. Our analysis indicates that EGCG significantly reduces ALT, AST, hepatic triglyceride, serum TG, hepatic TC, serum TC. The targets of EGCG may include antioxidants, regulation of lipid metabolism, anti-inflammation, improvement of insulin resistance, and inhibition of hepatic fibrosis. EGCG exerted its effects on NAFLD by modulating key signaling pathways, including PI3K/Akt/AMPK, TGF-β/Smad, Nrf2, NF-κB, and ROS/MAPK, highlighting its multifaceted mechanisms of action. The machine learning methods employed to ascertain the temporal relationship between the intervention and the outcome indicated that the optimal duration of the intervention was 10 to 15 weeks.

CONCLUSIONS

The efficacy of EGCG in treating NAFLD has been predicted within a time frame of 10-15 weeks. It may exert its effects primarily through the NF-κB and Nrf2 pathways, which regulate the ROS phenotype. EGCG may represent a promising target for the treatment of NAFLD.

Areas of research focus and trends in the research on the application of AIGC in healthcare

BACKGROUND

As a crucial part of current AI technology, the extent of AIGC's (Artificial Intelligence Generated Content) impact on healthcare, its potential to further drive the development of intelligent healthcare, and its ability to alleviate the current scarcity of medical resources have become highly discussed topics in academia and the healthcare sector. Our aim is to conduct bibliometric study to analyze and visualize the current research hotspots and trends related to the application of AIGC in healthcare.

METHODS

Studies related to the application of AIGC in healthcare that were published from November 30 2022, to December 31 2023, were retrieved via the Web of Science Core Collection. CiteSpace and Microsoft Excel were utilized to analyze and visualize the annual publications, countries, institutions, authors, journals, high cited literature and keywords.

RESULTS

A total of 3411papers were included, and the number of publications is increasing rapidly in recent two years. The United States and Harvard University are respectively the leading country and institution in terms of the number of research publications on the application of AIGC in healthcare. Thongprayoon Charat and Cheungpasitporn Wisit are outstanding investigators in this field. Nature is the most influential journal in the field of AIGC' s application in healthcare, whereas Cureus Journal of Medical Science boasts the highest number of publications on this topic. The analysis of keywords and high cited literature has identified current research hotspots, including the impact exploration of the application of AIGC in the healthcare industry, the assessment of applicability, the perception of healthcare related personnel, and the development of AIGC in the healthcare field.

CONCLUSIONS

This paper presents a bibliometric analysis of the research framework and hotspots concerning the application of AIGC in healthcare. The analysis aims to provide a comprehensive understanding of this field for researchers. Future research should focus on establishing regulatory mechanism, optimizing healthcare information services, clarifying the role of AIGC, and continuously developing medical large language models.

Application of deep learning-based facial pain recognition model for postoperative pain assessment

BACKGROUND

Postoperative pain is a common and complex issue that affects patients' recovery and the quality of healthcare. Traditional pain assessment methods-primarily based on self-reporting and clinical observation-are often inadequate, particularly for patients with communication impairments. Deep learning technology offers new opportunities for automatic pain assessment. However, progress in this area is hindered by the limited availability of high-quality clinical datasets and a paucity of studies addressing real-world model deployment. This gap between laboratory research and clinical application requires further study.

METHODS

The study constructed two distinct datasets to capture both clinical and laboratory scenarios. The Clinical Pain Dataset (CPD) includes 3411 facial pain images from 503 postoperative patients, while Simulated Pain Dataset (SPD) contains 1038 images from 51 volunteers. The two datasets were combined to form the Combined Dataset (CD). A pre-trained VGG16 model was used for training and validation. The model's performance on different datasets and pain levels was evaluated using area under the receiver operating characteristic curve (AUROC) and F1 scores.

RESULTS

In the CPD and CD, the model demonstrated its highest performance in identifying severe pain, achieving AUROC values of 0.898 (95 %CI,0.877-0.917) and 0.867 (95 %CI,0.844-0.889), respectively. For overall evaluation, the highest AUROC values were observed in CPD-train (0.898 [95 % CI: 0.877-0.917]) and CD-train (0.917 [95 % CI: 0.883-0.948]) for severe pain classification. Building on these results, a facial pain recognition software was developed based on the model, offering a new option for clinical pain identification.

CONCLUSIONS

The findings indicate that deep learning models leveraging facial expression analysis hold significant potential to recognize varying degrees of pain in clinical settings, especially severe pain. In the future, they could help anesthesiologists monitor postoperative patients' pain levels in real-time, enhancing the quality of medical services.

A case study of forensic psychiatry experts' reports analysis through large language models

The integration of artificial intelligence (AI) technologies in forensic psychiatry has gained significant attention due to their potential to enhance tasks such as outcome prediction and decision-making. In this study, we explored the feasibility and performance of a large language model (LLM-GPT) in extracting both clinical and non-clinical variables from authentic forensic psychiatric reports concerning defendants' criminal responsibility and social dangerousness. We employed GPT-4o to extract relevant data using a set of custom queries, which we applied to two forensic psychiatric expert reports. The results of the study demonstrated that the system was capable of extracting information from the forensic psychiatric reports and generating a summarized version. Identifying the most important parts to construct a meaningful synthesis in a highly specialized application domain is currently a challenge. This study highlights the potential of AI in forensic psychiatry and suggests that this approach could be valuable for collecting semi-automated or automated data from reports, enabling the creation of a large dataset that could be used for further research and analysis.

Knowledge and attitudes toward pediatric pain management among final-year medical students in Italy: a multicenter observational study

Pain management in pediatric patients is a critical aspect of healthcare, yet there is limited research on the knowledge and approaches of medical students regarding this topic. This study investigates the approach to pediatric pain management among final-year medical students. A cross-sectional survey was administered to final-year medical students from six universities located in Northern, Central, and Southern Italy between May and July 2024. The questionnaire (16 items) assessed the students' knowledge and attitudes toward acute pain management in pediatric patients. Descriptive and inferential statistics were used to analyze the data. A total of 321 students completed the survey. The majority (49%) of students reported limited training (< 1 h) in pediatric pain management during their studies. Only 42% of respondents believed that neonates feel pain more intensively than adults, while 38% thought the same for children up to 3 years. old While 81% recognized that pediatric pain can be measured with age-specific tools, 60% would avoid using opioids for severe pain in children. Seventy-five percent of students reported routinely using analgesia before painful procedures, but 33% would not treat abdominal pain before surgical evaluation in a suspected appendicitis case. Conclusion: There are relevant gaps in the education of medical students on pediatric pain management in Italy. Traditional old views on pain are widespread among final-year medical students. Targeted educational interventions are needed to address these issues and ensure that future healthcare providers are adequately prepared to manage pediatric pain. What is Known • Pediatric pain is often undertreated due to misconceptions among healthcare providers about their pain perception and the long-term consequences of untreated pain. • Studies investigating knowledge and attitudes toward pediatric pain management have primarily focused on physicians or nurses, with limited evidence on medical students' preparation in this area. What is New • This study reveals significant gaps in knowledge and outdated attitudes toward pediatric pain management among final-year medical students in Italy • Barriers to the use of opioids for severe pain management in children are already widespread among medical students.

A Retrospective Study of Lumbar Disk Herniation: An Analysis of Clinical Cases and Treatment Plans

Background/Objectives: One common musculoskeletal disorder seriously compromising quality of life and burdening healthcare systems is lumbar disk herniation (LDH). LDH affects quality of life, healthcare costs, and occupational productivity, and it is expected to affect 40% of the population, mostly between 30 and 50 years of age. Methods: Over three years, this research assessed treatment results and the effect of comorbidities in a sample of 3074 individuals. Results: Surgical treatments-especially microdiscectomy-have a high success rate; over 90% of patients said their pain and functioning six months after surgery had improved significantly. Comparatively, conservative treatment approaches-physical therapy and epidural steroid injections-showed about 60% success in 70% of patients, indicating a clear need for early surgical assessment since 25% of originally conservatively managed patients needed surgery within one year. Significantly affecting treatment success are demographic variables; patients with preoperative Oswestry Disability Index (ODI) scores above 50 saw a 40-point improvement post-surgery compared to a 20-point gain for those following conservative therapy. High comorbidity rates-including obesity (mean of 148.33) and cardiovascular illnesses (mean of 530.33)-that are associated with extended recovery durations and complications were also seen in this research. Conclusions: Our results support a customized treatment plan, stressing the need of integrating thorough rehabilitation plans with prompt surgical interventions to maximize patient outcomes. This study emphasizes the need for a patient-centered treatment paradigm in controlling LDH, thereby trying to improve recovery and lower the healthcare load.

Ketamine/Midazolam versus Fentanyl/Midazolam Sedation for Interventional Radiology Procedures: A Prospective Registry

PURPOSE

To assess whether sedation with ketamine/midazolam was more effective than fentanyl/midazolam at reducing periprocedural pain scores for interventional radiology (IR) procedures.

MATERIALS AND METHODS

Data on preprocedural, intraprocedural, and postprocedural pain scores, procedure duration, and moderate or worse adverse events (AEs) were collected as part of a prospective quality improvement registry before and after the introduction of a ketamine/midazolam sedation program at a single academic center, including 292 procedures performed on adult patients from April 2024 to August 2024. All IR staff were surveyed before and after the introduction of ketamine regarding their observations on sedation, with 23 respondents at baseline survey and 22 at follow-up.

RESULTS

Compared with fentanyl/midazolam sedation, ketamine/midazolam sedation was associated with lower intraprocedural (P ≤ .001) and postprocedural (P ≤ .05) pain scores, without prolonging procedure duration (P = .436) or increasing AEs (P > .999). The effect on pain scores was observed for biopsy and drainage, but not for venous port procedures. Staff reported that ketamine/midazolam sedation provided adequate comfort more often than fentanyl/midazolam sedation (P ≤ .01), and at study conclusion, most (82%) reported that they would choose ketamine/midazolam sedation for themselves.

CONCLUSIONS

Compared with fentanyl/midazolam, ketamine/midazolam sedation was superior regarding reduction of patient discomfort and preference by IR staff, with no added procedural duration or AEs when administered in the absence of anesthesiology providers. Findings suggest further investigation into incorporating ketamine into routine use in IR programs.

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Do clinical practice guidelines for low back pain include predatory journal or retracted publications? A meta-research study

BACKGROUND

Following clinical practice guidelines is widely recommended for treating many musculoskeletal diagnoses, including low back pain, but it is unknown if clinical practice guidelines for low back pain do.

OBJECTIVE

Assess whether clinical practice guidelines for low back pain reference publications from predatory journals or include retracted publications.

DESIGN

Meta-research.

METHODS

Clinical practice guidelines focusing on the management of adults with low back pain published between January 2010-June 2024 were included. All referenced publications in each guideline were evaluated for predatory categorization using a systematic process that included assessing publisher/journal websites, the Directory of Open Access Journals, Beall's List and major literature databases. The Retraction Watch Database was used to assess retraction status.

RESULTS

Nineteen clinical practice guidelines with 1617 unique publications met inclusion criteria. The majority of publications (1598/1617; 98.8 %) were categorized as 'non-predatory.' Fourteen publications (0.9 %) were categorized as 'predatory,' two (0.1 %) 'presumed predatory,' and three (0.2 %) were retracted. Four guidelines cited 'predatory' and/or 'presumed predatory' publications, and four guidelines cited the retracted publications.

CONCLUSION

Only 1.2 % of the cited publications included in clinical practice guidelines for the management of low back pain were deemed predatory or retracted, implying that guideline recommendations are unlikely to be influenced by their inclusion. There are currently no standard criteria for how to manage the inclusion of these publications in guidelines or systematic reviews. Future research should investigate the development of a valid and reliable checklist that allows authors to assess for and manage the presence of predatory or retracted publications.

Epigallocatechin 3-gallate-induced neuroprotection in neurodegenerative diseases: molecular mechanisms and clinical insights

Neurodegenerative diseases (NDs) are caused by progressive neuronal death and cognitive decline. Epigallocatechin 3-gallate (EGCG) is a polyphenolic molecule in green tea as a neuroprotective agent. This review evaluates the therapeutic effects of EGCG and explores the molecular mechanisms that show its neuroprotective properties. EGCG protects neurons in several ways, such as by lowering oxidative stress, stopping Aβ from aggregation together, changing cell signaling pathways, and decreasing inflammation. Furthermore, it promotes autophagy and improves mitochondrial activity, supporting neuronal survival. Clinical studies have demonstrated that EGCG supplementation can reduce neurodegenerative biomarkers and enhance cognitive function. This review provides insights into the molecular mechanisms and therapeutic potential of EGCG in treating various NDs. EGCG reduces oxidative stress by scavenging free radicals and enhancing antioxidant enzyme activity, aiding neuronal defense. It also protects neurons and improves cognitive abilities by inhibiting the toxicity and aggregation of Aβ peptides. It changes important cell signaling pathways like Nrf2, PI3K/Akt, and MAPK, which are necessary for cell survival, cell death, and inflammation. Additionally, it has strong anti-inflammatory properties because it inhibits microglial activation and downregulates pro-inflammatory cytokines. It improves mitochondrial function by reducing oxidative stress, increasing ATP synthesis, and promoting mitochondrial biogenesis, which promotes neurons' survival and energy metabolism. In addition, it also triggers autophagy, a cellular process that breaks down and recycles damaged proteins and organelles, eliminating neurotoxic aggregates and maintaining cellular homeostasis. Moreover, it holds significant promise as an ND treatment, but future research should focus on increasing bioavailability and understanding its long-term clinical effects. Future studies should focus on improving EGCG delivery and understanding its long-term effects in therapeutic settings. It can potentially be a therapeutic agent for managing NDs, indicating a need for further research.

Pain Experiences and Coping Methods of Living Liver Donors Experiencing Chronic Pain After Surgery: Qualitative Study

BACKGROUND

Living liver donors are known to experience many physical symptoms such as pain in the postoperative period.

AIM

This qualitative study was conducted to examine pain experiences and coping methods of living liver donors experiencing postoperative chronic pain.

METHODS

The study sample consisted of 16 living liver donors. Data were collected using a personal information form and a semi-structured questionnaire. Quantitative data were analyzed as number, mean, standard deviation, and percentage. Qualitative data were analyzed within the framework of thematic and content analysis methods.

RESULTS

The mean postoperative chronic pain rate of living liver donors was 4.5. Eight of these people reported experiencing sudden, pinprick-like pain after liver transplantation. Living liver donors stated that the greatest factor that increased their pain was exertion/spontaneous movement. Half of them stated that the pain they experienced after transplantation had no impact on their daily life activities. Most of the living liver donors stated that the pain they experienced had no impact on their social lives; however, it caused sadness and psychological deterioration. The most preferred method to cope with pain was the use of medication.

CONCLUSIONS

Living liver donors experienced moderate postoperative chronic pain, and a significant proportion of them reported psychological symptoms related to the pain they experienced. A multidisciplinary pain team can help with pain management by providing training conducted in transplant centers.

The Role of Biomarkers in Acute Pain: A Narrative Review

Acute pain, a critical aspect of patient care, presents a challenge due to its subjective nature and complex biological underpinnings. Biomarkers for acute pain promise a paradigm shift in how pain is perceived, diagnosed, and managed. The study of genetic, inflammatory, and neurotransmission markers associated with pain experience may hold the key for the development of personalized and effective pain management strategies. This narrative review explores the neurobiological pathways of acute pain, encompassing inflammatory responses and neurotransmission mechanisms. It synthesizes current research on the identification and clinical application of biomarkers, emphasizing their potential to enhance diagnostic precision, treatment effectiveness, and risk prediction. We underscore the promising role of acute pain biomarkers in identifying patients at risk for developing acute and potentially chronic pain, predicting patients' response to pharmacological interventions, and aiding in the development of novel therapeutic and pain preventive strategies. The evolving landscape of biomarker research not only deepens our understanding of pain mechanisms but also lays the foundation for more tailored and patient-specific healthcare interventions.

Generative AI vs. human expertise: a comparative analysis of case-based rational pharmacotherapy question generation

PURPOSE

This study evaluated the performance of three generative AI models-ChatGPT- 4o, Gemini 1.5 Advanced Pro, and Claude 3.5 Sonnet-in producing case-based rational pharmacology questions compared to expert educators.

METHODS

Using one-shot prompting, 60 questions (20 per model) addressing essential hypertension and type 2 diabetes subjects were generated. A multidisciplinary panel categorized questions by usability (no revisions needed, minor or major revisions required, or unusable). Subsequently, 24 AI-generated and 8 expert-created questions were asked to 103 medical students in a real-world exam setting. Performance metrics, including correct response rate, discrimination index, and identification of nonfunctional distractors, were analyzed.

RESULTS

No statistically significant differences were found between AI-generated and expert-created questions, with mean correct response rates surpassing 50% and discrimination indices consistently equal to or above 0.20. Claude produced the highest proportion of error-free items (12/20), whereas ChatGPT exhibited the fewest unusable items (5/20). Expert revisions required approximately one minute per AI-generated question, representing a substantial efficiency gain over manual question preperation. Nonetheless, 19 out of 60 AI-generated questions were deemed unusable, highlighting the necessity of expert oversight.

CONCLUSION

Large language models can profoundly accelerate the development of high-quality assessment questions in medical education. However, expert review remains critical to address lapses in reliability and validity. A hybrid model, integrating AI-driven efficiencies with rigorous expert validation, may offer an optimal approach for enhancing educational outcomes.

Examining the role of ChatGPT in promoting health behaviors and lifestyle changes among cancer patients

Purpose: This study aims to investigate the role of ChatGPT in promoting health behavioral changes among cancer patients. Methods: A quasi-experiment design with qualitative approach was adopted in this study, as the ChatGPT technology is novel, and many people are unaware of it. The participants included outpatients at a public hospital. An experiment was carried out, where the participants used ChatGPT for seeking cancer related information for two weeks, which is then followed by focus group (FG) discussions. A total of 72 outpatients participated in ten focus groups. Results: Three main themes with 14 sub-themes were identified reflecting the role of ChatGPT in promoting health behavior changes. Its prominent role was observed in developing health literacy, promoting self-management of conditions through emotional, informational, motivational support. Three challenges including privacy, lack of personalization, and reliability issues were identified. Conclusion: Although ChatGPT has a huge potential in promoting health behavior changes among cancer patients, its ability is minimized by several factors such as regulatory, reliability, and privacy issues. There is a need for further evidence to generalize the results across the regions.

Challenge of tracheal extubation and tube exchange of a difficult airway: a narrative review

Tracheal extubation is an integral part of airway management. Even though available data indicated that the incidence of complications immediately after tracheal extubation may be higher than during tracheal intubation, it is significantly underexplored in the scientific literature in comparison with tracheal intubation. Failure to re-secure the airway during or immediately after tracheal extubation may have fatal consequences. Closed claims analyses have highlighted the seriousness of adverse events occurring postextubation. Consequently, a well-planned strategy for tracheal extubation is as important as for the intubation and is correctly regarded as a logical extension of an intubation strategy. This narrative review focusses on the challenges of tracheal extubation and complications of routine and 'at-risk' extubation in adults. It provides the reader with a risk stratification before extubation. Guidelines for tracheal extubation including advanced techniques for tracheal extubation of patients 'at-risk' are followed by a detailed step-by-step approach for video-assisted tracheal tube exchange in patients with a difficult airway.

Fibromyalgia: are you a genetic/environmental disease?

Fibromyalgia, characterized by chronic widespread pain and fatigue, involves complex interactions between genetic predispositions and environmental triggers. This review delves into the multifaceted nature of fibromyalgia, emphasizing recent advances in understanding its pathogenesis through genetic, epigenetic, and environmental lenses. We explore the roles of specific genetic polymorphisms, such as those in the catechol-O-methyltransferase and serotonin transporter genes, and their correlation with the syndrome's susceptibility. The review also examines the significant impact of environmental factors, including physical trauma and stress, which potentiate the syndrome's severity. In addition, emerging research on the microbiome and epigenetic modifications provides new insights into the disease mechanisms, potentially guiding future therapeutic strategies. This article aims to synthesize current research findings and propose directions for future research, underscoring the necessity of a multidisciplinary approach to decipher the complexities of fibromyalgia.

Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China

Background: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and dietary factors. However, nutrition-related exposures have rarely been integrated into existing CRC risk prediction models. This study aimed to develop and validate a lifestyle-based 10-year CRC risk prediction model using longitudinal data from a large-scale population-based screening cohort to facilitate early risk stratification and personalized screening strategies. Methods: Data were obtained from 21,358 individuals participating in a CRC screening program in Shanghai, China, with over 10 years of active follow-up until 30 June 2021. Of these participants, 16,782 aged ≥40 years were used for model development, and 4576 for external validation. Predictors were selected using random survival forest (RSF) and elastic net methods, and the final model was developed using Cox regression. Machine learning approaches (RSF and XGBoost) were additionally applied for performance comparison. Model performance was evaluated through discrimination, calibration, and decision curve analysis (DCA). Results: The final model incorporated twelve predictors: age, gender, family history of CRC, diabetes, fecal immunochemical test (FIT) results, and seven lifestyle-related factors (smoking, alcohol use, body shape, red meat intake, fried food intake, pickled food intake, and fruit and vegetable intake). Compared to the baseline demographic-only model (C-index = 0.622; 95% CI: 0.589-0.657), the addition of FIT improved discrimination, and further inclusion of dietary and lifestyle variables significantly enhanced the model's predictive accuracy (C-index = 0.718; 95% CI: 0.682-0.762; ΔC-index = 0.096, p = 0.003). Conclusions: Incorporating dietary and lifestyle variables improved CRC risk stratification. These findings highlight the value of dietary factors in informing personalized screening decisions and providing an evidence-based foundation for targeted preventive interventions.

AI for chronic pain in children: a powerful resource

Given the lack of scientific evidence, chronic pain represents an arduous challenge, especially in the pediatric field. In this complex scenario, artificial intelligence (AI) could support diagnosis, therapy, and research. However, the great potential of AI must be combined with the protection of data and the most fragile patients.

T-Cell Engager Therapy in Prostate Cancer: Molecular Insights into a New Frontier in Immunotherapy

Advanced prostate cancer (PCa) remains lethal despite standard therapies, and immune checkpoint inhibitors offer limited benefit in its "immune-cold" microenvironment. T-cell engagers (TCEs)-bispecific antibodies linking CD3 on T-cells to tumor-associated antigens (TAAs)-provide potent, MHC-independent cytotoxicity, overcoming a key resistance mechanism. While early PSMA-targeted TCEs established proof-of-concept, recent data, notably for six transmembrane epithelial antigen of the prostate 1 (STEAP1)-targeting agents like Xaluritamig, demonstrate more substantial objective responses, highlighting progress through improved target selection and molecular design. This review synthesizes the evolving landscape of TCEs targeting PSMA, STEAP1, and DLL3 in PCa. We critically evaluate emerging clinical evidence, arguing that realizing the significant therapeutic potential of TCEs requires overcoming key challenges, including cytokine release syndrome (CRS), limited response durability, and antigen escape. We contend that future success hinges on sophisticated engineering strategies (e.g., affinity tuning, masking, multispecific constructs) and rationally designed combination therapies tailored to disease-specific hurdles. Strategies for toxicity mitigation, the crucial role of biomarker-driven patient selection, and potential integration with existing treatments are also discussed. Accumulating evidence supports TCEs becoming a new therapeutic pillar for advanced PCa, but achieving this demands sustained innovation focused on optimizing efficacy and safety. This review critically connects molecular engineering advancements with clinical realities and future imperatives.

Artificial Intelligence Use in Medical Education: Best Practices and Future Directions

PURPOSEOF REVIEW

This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS

AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in AI design and development, we can best preserve the integrity, quality, and clinical relevance of AI-generated content. In adopting the best practices for AI use, we can maximize its benefits while preserving the ethical standards of MedEd with the goal of improving learning outcomes.

Non-convulsive status epilepticus as a cause of delayed emergence after a thoracic surgery: a case report

Non-convulsive status epilepticus (NCSE) is an electrical discharge which occurs without prominent motor symptoms. NCSE is one of the causes of delayed emergence from anesthesia; however, as far as we know, previous reports of postoperative NCSE were related to patients after neurological surgery. Herein, we report a case of an elderly male who developed initial NCSE after thoracic surgery. The patient remained unresponsive and developed hemiplegia after lung resection, and then the symptoms fluctuated between better and worse. Metabolic disorders and stroke were ruled out, and NCSE was diagnosed by magnetic resonance imaging (MRI) and electroencephalography (EEG). NCSE occurred in a patient who had no predisposing factors or underwent non-neurological surgery. When anesthesiologists encounter delayed emergence, NCSE should be listed as a differential diagnosis and examined by MRI and EEG.

A cross-sectional study on pain neurophysiology knowledge among Spanish primary healthcare professionals

Healthcare professionals' (HCPs) knowledge and beliefs about pain are known to influence the treatment outcomes. The objective of this descriptive cross-sectional study was to evaluate the knowledge of the neurophysiology of pain among HCPs working with pain patients in primary care. Physiotherapists, nurses, physicians, nursing assistants, social workers and dentists of primary care were surveyed using the Neurophysiology of Pain Questionnaire (NPQ)(range 0-19) between September-December of 2022. Data on demographics, job-related information, own chronic musculoskeletal pain experience and specific training in the management/treatment of chronic musculoskeletal pain were collected and analyzed. Out of 1265 HCPs contacted, 510 (40.3%) participated and completed the survey. They were mostly women (78%), with a mean age of 49.5 ± 11.9 years. The NPQ score was 12.8 ± 2. Physiotherapists scored highest (14.4 ± 2). HCPs with chronic musculoskeletal pain scored less than those without, 12.5 ± 2 vs. 13 ± 1.8 (p < 0.01). The most reported areas of pain were the lumbar spine (25.3%) and neck (24.5%). No differences were found between sexes, age or healthcare profession. Primary HCPs demonstrated low to moderate levels of knowledge in pain neurophysiology, with room for improvement, particularly among those experiencing chronic musculoskeletal pain themselves. These findings suggest a need for enhanced pain management training, both within university curricula and through ongoing professional development programs for HCPs.

Physician awareness of, interest in, and current use of artificial intelligence large language model-based virtual assistants

There is increasing medical interest and research regarding the potential of large language model-based virtual assistants in healthcare. It is important to understand physicians' interest in implementing these tools into clinical practice, so preceding education could be implemented to ensure appropriate and ethical use. We aimed to assess physician 1) awareness of, 2) interest in, and 3) current use of large language model-based virtual assistants for clinical practice and professional development and determine the specific applications of interest and use. Additionally, we wanted to determine associations with age, gender, and role. We conducted a cross-sectional study between 11/08-12/2023 via an anonymous web-based survey that was disseminated among physicians at a large NY healthcare network using snowball sampling. Descriptive and basic inferential statistics were performed. There were 562 respondents, largely males (55.7%), attending physicians (68.5%), and from nonsurgical specialties (67.4%). Most were aware of large language model chatbots (89.7%) and expressed interest (97.2%). Only a minority incorporated it into their practice (21%). Highest levels of interest were for journal review, patient education, and documentation/dictation (88.1-89.5%). The most frequently employed uses were medical information and education and study/research design. Females showed higher interest than males (99.2% vs. 95.5%, p = 0.011). Attendings were more aware of large language models (92.2% vs. 84.2%, p = 0.004), while trainees had increased rates of use (28.8% vs. 17.4%, p = 0.002). Use varied across age brackets, highest among 20-30 year olds (29.1% vs. 13.5%-23.4%, p = 0.018), except for documentation/dictation, where highest use was among the 41-50 year old group (10.5% vs. 2.6%-8.7%, p = 0.047). We concluded that physicians are interested in large language model-based virtual assistants, a minority are implementing it into their practice, and gender-, role-, and age-based disparities exist. As physicians continue to integrate large language models into their patient care and professional development, there is opportunity for research, education, and guidance to ensure an inclusive, responsible, and safe adoption.

Personalized therapy in multiple sclerosis: an Italian Delphi consensus

OBJECTIVE

The increasing availability of disease-modifying therapies (DMTs) may provide more personalized treatment options for multiple sclerosis (MS) based on various factors, including patients' characteristics, prognostic indicators, comorbidities, and safety. In Italy, recent efforts focused on promoting interdisciplinary, patient-centered care and equitable access to optimized therapies, as reported in the 2023 Barometer of Multiple Sclerosis and Related Diseases from the Italian Multiple Sclerosis Association. A key challenge is ensuring equitable access to homogeneous and personalized therapeutic strategies.

MATERIALS AND METHODS

Using a Delphi methodology, a panel of Italian neurologists with expertise in MS evaluated consensus on specific aspects of MS treatments, including personalized therapy, patient involvement in decision-making, treatment flexibility, self-management of therapies, perception of treatment efficacy and safety and therapeutic sequence management.

RESULTS

Of 166 votes, 116 statements reached consensus (68% positive, 2% negative), representing 70% of the total, whereas 50 (30%) highlighted areas of non-consensus. The findings emphasize the central role of neurologists, the importance of personalized therapy, the inclusion of patients in therapeutic choices to enhance adherence and quality of life, and managing both quality of life and caregiver burden. Most high-efficacy disease-modifying therapies (HE DMTs), like cladribine and anti-CD20 therapies, recognized for their efficacy and convenience of administration, received positive consensus, emphasizing their perceived value in individualized treatment approaches.

CONCLUSIONS

This research highlights best practices and provides a roadmap for improving patient outcomes through tailored, well-communicated therapeutic strategies.

Game on or game over? Gamification from 360-degree perspective, perception, confidence, and challenges in simulation based nursing education: mixed-method study

BACKGROUND

Gamification has emerged as a transformative approach in nursing education, especially within simulation-based learning environments. It is recognized for enhancing student engagement, knowledge retention, and confidence. Despite its potential, limited research has explored the perceptions and confidence of nurse educators and students, as well as the challenges encountered during its implementation. The study aimed to assess the perceptions and confidence of nurse educators and nursing students towards integrating gamification into simulation-based nursing education, identify implementation barriers, and develop and validate two psychometric tools: the Gamification Perception Assessment Tool and the Nurse Educator Confidence Tool.

METHODS

A convergent mixed-methods design was utilized, involving 115 nurse educators and 317 nursing students from eight nursing institutions in Cairo. Quantitative data were collected using the newly developed tools, which underwent rigorous validation through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and reliability testing. Qualitative data were collected via semi-structured questionnaires and interviews with nurse educators and analyzed thematically to explore implementation challenges.

RESULTS

The overall mean perception score was 34.8 ± 8.4 for nursing students and 36.3 ± 7.9 for nurse educators, with the majority of participants in both groups showing a high perception level (61.7% for educators and 58.9% for students). Nurse educators displayed moderate to high confidence, which was significantly influenced by their experience and prior training. A strong positive correlation (r = 0.711, p = 0.001) was found between perception and confidence. The psychometric tools demonstrated high reliability (Cronbach's α = 0.68-0.85) and model fit. Thematic analysis revealed barriers such as institutional policy gaps, limited IT support, and lack of training.

CONCLUSION

Gamification is positively perceived and fosters educator confidence in simulation-based nursing education. However, successful implementation requires institutional support, faculty training, and standardized evaluation tools to overcome existing challenges and optimize educational outcomes. The study provides validated tools and empirical insights into gamification in nursing education.

CLINICAL TRIAL NUMBER

Not applicable.

Transgenic zebrafish embryos to evaluate the in vivo effects of different liposome-paclitaxel nanocarrier system

Zebrafish is an established valuable model for understanding the complex in vivo behavior of systemic nanocarrier strategies, their safety profile, and the array of possible compositions. To date, we have explored the possibility of Paclitaxel (PTX) delivery using liposome systems as a promising approach to treating cancer. Despite its benefits, this efficacious anti-cancer drug presents a main adverse effect, such as the onset of chemotherapy-induced peripheral neuropathy (CIPN). Thus, many research efforts are aimed at searching for the resolution or reduction of such problems. Taking advantage of zebrafish embryos, a powerful model for predicting and translating what is observed in humans, we investigated the plain PTX outcomes and compared them to the effects of different liposomes loaded with PTX. Since approximately 70% of human genes have at least one orthologue in zebrafish, studying the molecular mechanisms underlying chemotherapy-induced toxicity and increased oxidative stress becomes easy. First, we used a transgenic model to evaluate the systemic response to different concentrations of PTX, planning the final concentration to be loaded in liposomes, with and without functionalization. Then, we assessed the effects of this promising nanocarrier system at a molecular, histochemical, and behavioral level in reducing the detrimental side effects of the most successful cancer drug.

Bridging the gap between scientists and clinicians: addressing collaboration challenges in clinical AI integration

This article explores challenges for bridging the gap between scientists and healthcare professionals in artifical intelligence (AI) integration. It highlights barriers, the role of interdisciplinary research centers, and the importance of diversity, equity, and inclusion. Collaboration, education, and ethical AI development are essential for optimizing AI's impact in perioperative medicine.

Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development

Post-COVID syndrome affects 10-35 % of COVID-19 patients, and up to 85 % of hospitalized individuals, underscoring the need for early identification of high-risk cases. We hypothesized that the proteomic profile of serum small extracellular vesicles (sEVs) obtained during acute SARS-CoV-2 infection could predict post-COVID syndrome. Serum samples from 59 patients, stratified as asymptomatic, moderate, or severe, were analyzed. sEVs were isolated, characterized by electron microscopy, nanoparticle tracking, and flow cytometry, then profiled via LC-MS. Classification models integrating comorbidities, acute symptoms, and sEV proteomics distinguished the three groups, with sEV data outperforming conventional measures. Of 620 identified proteins, 30 showed significant differences between symptomatic and asymptomatic patients, including 12 linked to complement activation. ELISA confirmed LC-MS results that serum sEVs of post-COVID patients had altered C1 inhibitor, C3, and C5 levels. These results suggest that sEV-based proteomics can enable earlier detection and more targeted follow-up for individuals at risk of post-COVID syndrome.

Advancements in Spinal Endoscopic Surgery: Comprehensive Techniques and Pathologies Addressed by Full Endoscopy Beyond Lumbar Disc Herniation

Endoscopic spine surgery (ESS) has traditionally been employed for lumbar disc herniation (LDH). Recent innovations in surgical methods and technologies have expanded its range to address other spinal pathologies, providing minimally invasive solutions with potential clinical benefits. Our review aims to summarize the applications, clinical outcomes, and limitations of ESS beyond LDH, focusing on its role in complex spinal conditions such as stenosis, thoracic disc herniation, spinal tumors, synovial cysts, and failed back surgery syndrome. A thorough review of the literature was conducted to assess and summarize the current evidence regarding ESS applications for spinal conditions beyond LDH surgery. Areas of focus included innovations in technology and technique, as well as comparisons with conventional open surgical methods. ESS shows notable potential across different spinal conditions by providing minimally invasive alternatives to traditional open surgery. Its use could be associated with reduced surgical morbidity, shorter recovery times, and improved patient outcomes. In particular, ESS is versatile in addressing both degenerative and neoplastic conditions of the spine. Despite this, challenges such as technical complexity, steep learning curves, and limited indications for certain pathologies remain as barriers to wider adoption. ESS is evolving in spine surgery, extending its utility beyond LDH surgery. While the current evidence largely supports its clinical efficacy, further studies are needed to address the present limitations and optimize its application. Future developments in surgical training and technology will likely enhance its adoption and broaden its clinical indications.

In vitro and in vivo anticancer activity of nickel (II) tetraazamacrocyclic diperchlorate complex, [(Ni-Me8[14]diene)(ClO4)2] against ehrlich ascites carcinoma (EAC) and MCF7 cells

Cancer remains a global health burden, with a pressing need for more effective treatments. This study uses a novel compound, Nickel (II) diperchlorate complex of the ligand (L): 3,10-C-meso-3,5,7,7,10,12,14,14-octamethyl-1,4,8,11-tetraazacyclotetradeca-4,11-diene, Me8[14]diene, designated as [Ni(II)L](ClO4)2, to explore its potential as an anticancer agent. Its efficacy was evaluated against Ehrlich Ascites Carcinoma (EAC)-bearing Swiss albino mice by monitoring tumor cell growth inhibition, survival time, tumor mass reduction, and hematological profiles. Additionally, cytotoxicity was investigated in vitro using MCF7 breast cancer cells. The apoptotic potential was evaluated through Hoechst staining, with changes in apoptosis-related gene expression (p53, BCL2, BAX, PARP1, CASP3, CASP8, and CASP9) using RT-qPCR. The test compound's toxicity was evaluated by monitoring hematological, biochemical, and histological changes. The compound exhibited dose-dependent growth inhibition of EAC cells with 88.45% inhibition at a dose of 200 µg/kg (p < 0.01), extended lifespan by 52.63%, reduced tumor weight by 47.83%, and restored hematological parameters in EAC-bearing mice. Cytotoxicity assays yielded LC50 and IC50 values of 23.73 µg/mL and 71.52 µg/mL, respectively. Apoptosis induction was evidenced by cell membrane blebbing, apoptotic body formation, chromosomal condensation, and nuclear fragmentation in MCF7 cells. Significant upregulation of pro-apoptotic genes such as p53, BAX, PARP1, CASP3, CASP8, and CASP9, alongside downregulation of anti-apoptotic gene BCL2, implied activation of the apoptotic pathway in cancer cells, followed by compound treatment. Moreover, no long-term negative impacts on tissue levels or hematological or biochemical markers were noted in the mice. Altogether, [Ni(II)L](ClO4)2 demonstrates promising anticancer activity and could serve as a potential chemotherapeutic agent, pending further studies.

Mucosal implications of oral Jak3-targeted drugs in COVID patients

The JAK family, particularly JAK3, plays a crucial role in immune signaling and inflammatory responses. Dysregulated JAK3 activation in SARS-CoV-2 infections has been associated with severe inflammation and respiratory complications, making JAK inhibitors a viable therapeutic option. However, their use raises concerns regarding immunosuppression, which could increase susceptibility to secondary infections. While long-term adverse effects are less of a concern in acute COVID-19 treatment, patient selection and monitoring remain critical. Furthermore, adverse effects associated with oral JAK3 inhibitors necessitate the exploration of alternative strategies to optimize therapeutic efficacy while minimizing risks. This review highlights the role of JAK3 in immune and epithelial cells, examines the adverse effects of oral JAK3 inhibitors in COVID-19 and other treatments, and discusses alternative therapeutic strategies for improving patient outcomes.

Comparative evaluation of six large language models in transfusion medicine: Addressing language and domain-specific challenges

BACKGROUND AND OBJECTIVES

Large language models (LLMs) such as GPT-4 are increasingly utilized in clinical and educational settings; however, their validity in subspecialized domains like transfusion medicine remains insufficiently characterized. This study assessed the performance of six LLMs on transfusion-related questions from Korean national licensing examinations for medical doctors (MDs) and medical technologists (MTs).

MATERIALS AND METHODS

A total of 23 MD and 67 MT questions (2020-2023) were extracted from publicly available sources. All items were originally written in Korean and subsequently translated into English to evaluate cross-linguistic performance. Each model received standardized multiple-choice prompts (five options), and correctness was determined by explicit answer selection. Accuracy was calculated as the proportion of correct responses, with 0.75 designated as the performance threshold. Chi-square tests were employed to analyse language-based differences.

RESULTS

GPT-4 and GPT-4o consistently surpassed the 0.75 threshold across both languages and examination types. GPT-3.5 demonstrated reasonable accuracy in English but showed a marked decline in Korean, suggesting limitations in multilingual generalization. Gemini 1.5 outperformed Gemini 1, particularly in Korean, though both exhibited variability across technical subdomains. Clova X showed inconsistent results across settings. All models demonstrated limited performance in legal and ethical scenarios.

CONCLUSION

GPT-4 and GPT-4o exhibited robust and reliable performance across a range of transfusion medicine topics. Nonetheless, inter-model and inter-language variability highlights the need for targeted fine-tuning, particularly in the context of local regulatory and ethical frameworks, to support safe and context-appropriate implementation in clinical practice.

Temporal Association Between ChatGPT-Generated Diarrhea Synonyms in Internet Search Queries and Emergency Department Visits for Diarrhea-Related Symptoms in South Korea: Exploratory Study

BACKGROUND

Diarrhea, a common symptom of gastrointestinal infections, can lead to severe complications and is a major cause of emergency department (ED) visits.

OBJECTIVE

This study explored the temporal association between internet search queries for diarrhea and its synonyms and ED visits for diarrhea-related symptoms.

METHODS

We used data from the National Emergency Department Information System (NEDIS) and NAVER (Naver Corporation), South Korea's leading search engine, from January 2017 to December 2021. After identifying diarrhea synonyms using ChatGPT, we compared weekly trends in relative search volumes (RSVs) for diarrhea, including its synonyms and weekly ED visits. Pearson correlation analysis and Granger causality tests were used to evaluate the relationship between RSVs and ED visits. We developed an Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to further predict these associations. This study also examined the age-based distribution of search behaviors and ED visits.

RESULTS

A significant correlation was observed between the weekly RSV for diarrhea and its synonyms and weekly ED visits for diarrhea-related symptoms (ranging from 0.14 to 0.51, P<.05). Weekly RSVs for diarrhea synonyms, such as "upset stomach," "watery diarrhea," and "acute enteritis," showed stronger correlations with weekly ED visits than weekly RSVs for the general term "diarrhea" (ranging from 0.20 to 0.41, P<.05). This may be because these synonyms better reflect layperson terminology. Notably, weekly RSV for "upset stomach" was significantly correlated with weekly ED visits for diarrhea and acute diarrhea at 1 and 2 weeks before the visit (P<.05). An ARIMAX model was developed to predict weekly ED visits based on weekly RSVs for diarrhea synonyms with lagged effects to capture their temporal influence. The age group of <50 years showed the highest activity in both web-based searches and ED visits for diarrhea-related symptoms.

CONCLUSIONS

This study demonstrates that weekly RSVs for diarrhea synonyms are associated with weekly ED visits for diarrhea-related symptoms. By encompassing a nationwide scope, this study broadens the existing methodology for syndromic surveillance using ED data and provides valuable insights for clinicians.

Benchmarking AI chatbots: assessing their accuracy in identifying hijacked medical journals

OBJECTIVES

The challenges posed by questionable journals to academia are very real, and being able to detect hijacked journals would be valuable to the research community. Using an artificial intelligence (AI) chatbot may be a promising approach to early detection. The purpose of this research is to analyze and benchmark the performance of different AI chatbots in identifying hijacked medical journals.

METHODS

This study utilized a dataset comprising 21 previously identified hijacked journals and 10 newly detected hijacked journals, alongside their respective legitimate versions. ChatGPT, Gemini, Copilot, DeepSeek, Qwen, Perplexity, and Claude were selected for benchmarking. Three question types were developed to assess AI chatbots' performance in providing information about hijacked journals, identifying hijacked websites, and verifying legitimate ones.

RESULTS

The results show that current AI chatbots can provide general information about hijacked journals, but cannot reliably identify either real or hijacked journal titles. While Copilot performed better than others, it was not error-free.

CONCLUSIONS

Current AI chatbots are not yet reliable for detecting hijacked journals and may inadvertently promote them.

Stem-Cell-Assisted Lipotransfer and Platelet-Rich Plasma in Breast Reconstruction: A Literature Review

BACKGROUND

Autologous fat transplantation to the breast, a minimal invasive technique, has significantly expanded in aesthetic and reconstructive surgery over the past two decades. Initially used for lumpectomy defects or correcting contour deformities post-breast reconstruction, it is now also performed as a stand-alone technique for primary breast augmentation or as a complementary method to implant-based augmentation. However, this surgical technique is associated with a high absorption rate. The use of fat grafts for breast augmentation remains controversial due to concerns about its safety, efficacy, and impact on breast cancer.

METHODS

This review examines the literature on cell-assisted lipotransfer (CAL) and PRP-assisted lipotransfer, comparing fat graft survival, complication rates, and oncological safety with conventional autologous lipotransfer.

RESULTS

CAL and PRP-assisted lipotransfer techniques demonstrate improved fat graft retention and reduced complications compared to conventional methods. Several studies report a potential reduction in the absorption rate of fat grafts with improvements in aesthetic outcomes.

CONCLUSIONS

While CAL and PRP-assisted lipotransfer have shown promising results in enhancing fat graft survival and reducing complications, there are still concerns about their oncological safety.

NO LEVEL ASSIGNED

This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Application and Perspectives of Immunotherapy in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma poses grave challenges to clinicians and patients due to its tumour invasiveness and treatment uncertainties. Despite the substantial improvements in conventional treatment modalities such as surgery, chemotherapy and radiotherapy, the recurrence rate and mortality rate of HNSCC remain stubbornly high. Traditionally, the front-line therapy for recurrent/metastatic HNSCC has been the amalgamation of platinum-based drugs/paclitaxel, 5-fluorouracil and cetuximab, yet it only improves the prognosis of some patients, and the overall treatment situation remains severe. Immunotherapy, as an emerging luminary in the domain of cancer treatment, is committed to improving the tumour microenvironment and stimulating the immune system to perform anti-tumour functions. The continuous updates of immune checkpoint inhibitors have also obtained favourable clinical feedback and are expected to overcome the constraints of traditional therapies. This article elaborates on the trends of immunotherapy within the TME and the progress of immunotherapy, aiming to provide new ideas for treatment regimens in the new therapeutic landscape of HNSCC and offer new hope for patients.

Implementing individualised physiotherapy using the Specific Treatment of Problems of the Spine (STOPS) approach for chronic low back pain in Bangladesh: Protocol for a prospective sequential comparison clinical trial

BACKGROUND

Chronic low back pain (CLBP) is a major global health issue, with a particularly high burden in low- and middle-income countries (LMICs) due partly to inadequate evidence-based management. The Specific Treatment of Problems of the Spine (STOPS) approach offers individualised, biopsychosocial-based physiotherapy shown to be effective in Australia. This trial aims to evaluate the implementation, effectiveness, and cost-effectiveness of the STOPS approach to individualised physiotherapy compared to usual physiotherapy care in Bangladesh.

METHODS

A three-phase prospective sequential comparison trial will be conducted with eligible participants aged 18-65 years presenting for physiotherapy with CLBP at two centres in Bangladesh. Phase 1 will evaluate the usual physiotherapy care, and Phase 3 will evaluate individualised physiotherapy according to the STOPS approach. Phase 2 involves training physiotherapists and assistants in STOPS. Patients in Phases 1 & 3 will undergo 10 sessions of physiotherapy over 10 weeks, with an 11th booster session at 6 months. Primary outcomes are activity limitation (Oswestry Disability Index) and pain intensity (Numerical Rating Scales for back and leg pain) at 5, 10, 26 and 52 weeks. Secondary outcomes include global effect, patient satisfaction, work interference, psychosocial risk factors, quality of life, mental health, sleep, catastrophizing and self-efficacy. Qualitative interviews will explore patient and therapist experiences.

RESULTS

Quantitative and qualitative data will be analysed to assess implementation feasibility, cost-effectiveness, and treatment outcomes.

CONCLUSION

Individualised physiotherapy using the STOPS approach could improve treatment outcomes for CLBP in Bangladesh.

CLINICAL TRIAL REGISTRATION

NCT05880212.

Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents' perspectives

OBJECTIVES

The aim of this study was to compare the performances of residents and ChatGPT in answering validated questions and assess paediatric surgery residents' acceptance, perceptions and readiness to integrate artificial intelligence (AI) into clinical practice.

METHODS

We conducted a cross-sectional study using randomly selected questions and clinical cases on paediatric surgery topics. We examined residents' acceptance of AI before and after comparing their results to ChatGPT's results using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data analysis was performed using Jamovi V.2.4.12.0.

RESULTS

30 residents participated. ChatGPT-4.0's median score was 13.75, while ChatGPT-3.5's was 8.75. The median score among residents was 8.13. Differences appeared statistically significant. ChatGPT outperformed residents specifically in definition questions (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 vs residents, p=0.03). In the UTAUT2 Questionnaire, respondents expressed a more positive evaluation of ChatGPT with higher mean values for each construct and lower fear of technology after learning about test scores.

DISCUSSION

ChatGPT performed better than residents in knowledge-based questions and simple clinical cases. The accuracy of ChatGPT declined when confronted with more complex questions. The UTAUT questionnaire results showed that learning about the potential of ChatGPT could lead to a shift in perception, resulting in a more positive attitude towards AI.

CONCLUSION

Our study reveals residents' positive receptivity towards AI, especially after being confronted with its efficacy. These results highlight the importance of integrating AI-related topics into medical curricula and residency to help future physicians and surgeons better understand the advantages and limitations of AI.

20-Hydroxyecdysone Modulates Bmp53-Mediated Apoptosis Regulation by Suppressing Mdm2-like-Dependent Ubiquitination in Silkworm, Bombyx mori

In the silkworm, 20-hydroxyecdysone (20E) induces apoptosis and autophagy, driving larval organ degeneration and remodeling. This mechanism may be a potential target for eco-friendly strategy for insect pests. However, a major challenge in harnessing this approach lies in the insufficient understanding of 20E's regulatory process in such a cell death mechanism. Our previous research has identified Bmp53 as a crucial gene in promoting the development of Bombyx mori during the pupal stage by inducing apoptosis, and has predicted the potential apoptotic regulatory network of Bmp53, wherein Mdm2-like ubiquitinating structural protein serves as a key component of this network. This study demonstrates that Mdm2-like acts as a ubiquitination regulatory protein, controlling its apoptosis-inducing activity via interaction with Bmp53. Moreover, co-expression of Mdm2-like and Bmp53 indirectly affects gene expression in the 20E-mediated apoptosis pathway. Further investigation revealed that Mdm2-like suppresses 20E-induced apoptosis by downregulating Bmp53 expression. This study reveals that the ubiquitination-mediated Mdm2-like/Bmp53 apoptosis pathway is a novel mechanism regulating silkworm apoptosis, with 20E playing a crucial role in this process. These findings enhance our understanding of the genetic mechanisms underlying tissue degradation during the metamorphic stage of the Bombyx mori. Additionally, these insights provide a theoretical reference for the development of environmentally friendly, hormone-based control strategies targeting protein modification as a means of managing lepidopteran pests.

Artificial Intelligence Chatbots as Sources of Implant Dentistry Information for the Public: Validity and Reliability Assessment

This study assessed the reliability and validity of responses from three chatbot systems-OpenAI's GPT-3.5, Gemini, and Copilot-concerning frequently asked questions (FAQs) in implant dentistry posed by patients.Twenty FAQs were prompted to three chatbots in three different times utilizing their respective application programming interfaces. The responses were assessed for validity (low and high threshold) and reliability by two prosthodontic consultants using a five-point Likert scale.The test of normality was utilized using the Shapiro-Wilk test. Differences between different chatbots regarding the quantitative variables in a given (fixed) time point and between the same chatbots in different time points were assessed using Friedman's two-way analysis of variance by ranks, followed by pairwise comparisons. All statistical analyses were conducted using the SPSS (Statistical Package for Social Sciences) Version 26.0 software program.GPT-3.5 provided the longest responses, while Gemini was the most concise. All chatbots advised consulting dental professionals more frequently. Validity was high under the low-threshold test but low under the high-threshold test, with Copilot scoring the highest. Reliability was high for all, with Gemini achieving perfect consistency.Chatbots showed consistent and generally valid responses with some variability in accuracy and details. While the chatbots demonstrated a high degree of reliability, their validity-especially under high-threshold criterion-remains limited. Improvements in accuracy and comprehensiveness are necessary for more effective use in providing information about dental implants.