Field Of Medicine Research Articles

Iliada psychoterapii

In analysing the case study of Sigmund Freud’s Dora, the author refers to the thoughts of Michel Foucault and James Hillman. Foucault treated the case study as an example of knowledge-power, while Hillman treated it as a type of literary fiction. Dora’s case study is, as Hillman observes, “the Iliad of psychotherapy”, the first great therapeutic story. Freud, combining the fields of medicine and literature, discovered a vehicle by which he could announce a new idea and method to the world. In this article, the author strives to show how fiction brought to life a new type of power, created truth, and convinced individuals to accept it.

The LUSBI Protocol (Lung Ultrasound/BREST Score/Inferior Vena Cava)-Its Role in a Differential Diagnostic Approach to Dyspnea of Cardiogenic and Non-Cardiogenic Origin.

Background and Objectives: PoCUS ultrasound applications are widely used in everyday work, especially in the field of emergency medicine. The main goal of this research was to create a diagnostic and therapeutic protocol that will integrate ultrasound examination of the lungs, ultrasound measurements of the inferior vena cava (assessment of central venous pressure) and BREST scores (risk stratification for heart failure), with the aim of establishing a more effective differential diagnostic approach for dyspneic patients. Materials and Methods: A cross-sectional study was conducted in the emergency medicine department with the educational center of the community health center of Banja Luka. Eighty patients of both sexes were included and divided into experimental and control groups based on the presence or absence of dyspnea as a dominant subjective complaint. Based on the abovementioned variables, the LUSBI protocol (lung ultrasound/BREST score/inferior vena cava) was created, including profiles to determine the nature of the origin of complaints. The biochemical marker of heart failure NT pro-BNP served as a laboratory confirmation of the cardiac origin of the complaints. Results: The distribution of NT pro BNP values in the experimental group showed statistically significant differences between individual profiles of the LUSBI protocol (p < 0.001). Patients assigned to group B PLAPS 2 had significantly higher average values of NT pro-BNP (20159.00 ± 3114.02 pg/mL) compared to other LUSBI profiles. Patients from the experimental group who had a high risk of heart failure according to their BREST scores also had a significantly higher average maximum expiratory diameter compared to those without heart failure (p = 0.004). A statistically significant difference (p = 0.001) in LUSBI profiles was observed between the groups of patients divided according to CVP categories. Conclusion: The integration of the LUSBI protocol into the differential diagnosis of dyspnea has been shown to be very effective in confirming or excluding a cardiac cause of the disease in patients.

Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

Declining mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory. Develop machine-learning models for identifying acquired neurologic morbidity while hospitalized with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers. Retrospective cohort study. Two large, quaternary children's hospitals. Critical illness. The outcome was neurologic morbidity, defined according to a computable, composite definition at the development site or an order for neurocritical care consultation at the validation site. Models were developed using varying time windows for temporal feature engineering and varying censored time horizons prior to identified neurologic morbidity. Optimal models were selected based on F1 scores, cohort sizes, calibration, and data availability for eventual deployment. A generalizable created at the development site was assessed at an external validation site and optimized with spline recalibration. Correlation was assessed between development site model predictions and measurements of brain biomarkers from a convenience cohort. After exclusions there were 14,222-25,171 encounters from 2010-2022 in the development site cohorts and 6,280-6,373 from 2018-2021 in the validation site cohort. At the development site, an extreme gradient boosted model (XGBoost) with a 12-hour time horizon and 48-hour feature engineering window had an F1-score of 0.54, area under the receiver operating characteristics curve (AUROC) of 0.82, and a number needed to alert (NNA) of 2. A generalizable XGBoost model with a 24-hour time horizon and 48-hour feature engineering window demonstrated an F1-score of 0.37, AUROC of 0.81, AUPRC of 0.51, and NNA of 4 at the validation site. After recalibration at the validation site, the Brier score was 0.04. Serum levels of the brain injury biomarker glial fibrillary acidic protein measurements significantly correlated with model output (r s =0.34; P =0.007). We demonstrate a well-performing ensemble of models for predicting neurologic morbidity in children with biomolecular corroboration. Prospective assessment and refinement of biomarker-coupled risk models in pediatric critical illness is warranted. Question Can interoperable models for predicting neurological deterioration in critically ill children be developed, correlated with serum-based brain-derived biomarkers, and validated at an external site? Findings A development site model demonstrated an area under the receiver operating characteristics curve (AUROC) of 0.82 and a number needed to alert (NNA) of 2. Predictions correlated with levels of glial fibrillary acidic protein in a subset of children. A generalizable model demonstrated an AUROC of 0.81 and NNA of 4 at the validation site. Meaning Well performing prediction models coupled with brain biomarkers may help to identify critically ill children at risk for acquired neurological morbidity.

Dr. Hulusi Behçet: A Pioneer in Dermatology and the Legacy of Behçet's Disease.

Dr. Hulusi Behçet (1889-1948) was a renowned Turkish dermatologist who made significant contributions in the field. His innovative research led to the discovery of Behçet's disease, a complex autoimmune disorder characterised by recurring mouth ulcers, inflammation of the eye, genital ulcers, and skin lesions. Behçet was born in Istanbul and had a comprehensive medical education, which he expanded through subsequent studies in Europe. His medical career was marked by his astute observations and rigorous study, ultimately leading to the official identification of Behçet's disease in 1937. Behçet wrote more than 100 publications and made substantial contributions to the fields of dermatology and internal medicine, particularly in the areas of syphilis and cutaneous leishmaniasis research. The impact of his work may still be seen in the ongoing investigation and management of Behçet's illness, leading to enhanced well-being for numerous individuals across the globe. Dr. Behçet's commitment to medical science is honoured by the recognition of Behçet's disease, academic accolades, and institutions named after him. His work continues to be a fundamental aspect in the examination of autoimmune illnesses, demonstrating the lasting influence of his contributions to the field of medical science.

Enhancing wound healing with zinc and silver nanocomposites synthesized with β-lactoglobulin: antimicrobial properties, collagen deposition, and systemic effects in a C57BL/6J mouse model

This study explores the potential of zinc and silver nanocomposites, synthesized with β-lactoglobulin, a whey protein, in promoting wound healing, using the C57BL/6J mouse model. Our research is distinct in its dual focus: assessing the antimicrobial efficacy of these nanocomposites and their impact on wound healing processes. The antimicrobial properties were investigated through minimum inhibitory concentration (MIC) assessments and colony-forming unit (CFU) tests, providing insights into their effectiveness against wound-associated microorganisms. Notably, the formulation's effective antibacterial concentration did not exhibit toxicity to mouse fibroblasts. A key aspect of our methodology involved the use of a stereoscopic microscope for detailed monitoring of the wound closure process. Additionally, the distribution and potential systemic effects of the zinc and silver ions were analyzed using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). This analysis was crucial in evaluating metal ion absorption through the wound site and estimating any toxic effects on the body. Our findings are particularly significant in the field of regenerative medicine. Transmission electron microscopy (TEM) revealed that the tested nanocomposites notably enhanced collagen deposition, a vital component in the wound healing process. Furthermore, a reduction in glycogen levels in hepatocytes was observed following treatment with these metal-protein dressings. This novel finding warrants further investigation. Overall, our findings highlight the diverse roles of zinc and silver nanocomposites in wound healing. This study not only contributes to our understanding of metal-protein complexes in tissue regeneration but also opens new avenues for research into the delivery mechanisms of such treatments for hard-to-heal wounds.

Performance of ChatGPT-3.5 and GPT-4 in national licensing examinations for medicine, pharmacy, dentistry, and nursing: a systematic review and meta-analysis

BackgroundChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models’ performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis.MethodsFollowing the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT’s introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model.ResultsA total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis.ConclusionsThis study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots.

Performance of ChatGPT in the In-Training Examination for Anesthesiology and Pain Medicine Residents in South Korea: Observational Study.

ChatGPT has been tested in health care, including the US Medical Licensing Examination and specialty exams, showing near-passing results. Its performance in the field of anesthesiology has been assessed using English board examination questions; however, its effectiveness in Korea remains unexplored. This study investigated the problem-solving performance of ChatGPT in the fields of anesthesiology and pain medicine in the Korean language context, highlighted advancements in artificial intelligence (AI), and explored its potential applications in medical education. We investigated the performance (number of correct answers/number of questions) of GPT-4, GPT-3.5, and CLOVA X in the fields of anesthesiology and pain medicine, using in-training examinations that have been administered to Korean anesthesiology residents over the past 5 years, with an annual composition of 100 questions. Questions containing images, diagrams, or photographs were excluded from the analysis. Furthermore, to assess the performance differences of the GPT across different languages, we conducted a comparative analysis of the GPT-4's problem-solving proficiency using both the original Korean texts and their English translations. A total of 398 questions were analyzed. GPT-4 (67.8%) demonstrated a significantly better overall performance than GPT-3.5 (37.2%) and CLOVA-X (36.7%). However, GPT-3.5 and CLOVA X did not show significant differences in their overall performance. Additionally, the GPT-4 showed superior performance on questions translated into English, indicating a language processing discrepancy (English: 75.4% vs Korean: 67.8%; difference 7.5%; 95% CI 3.1%-11.9%; P=.001). This study underscores the potential of AI tools, such as ChatGPT, in medical education and practice but emphasizes the need for cautious application and further refinement, especially in non-English medical contexts. The findings suggest that although AI advancements are promising, they require careful evaluation and development to ensure acceptable performance across diverse linguistic and professional settings.

Investigation of Cytotoxic Effects of Tragopogon reticulatus Against HepG2 and MCF-7 Cell Lines and Antioxidant, Antimicrobial Effects Against Some Microorganisms

Plants have been used for various purposes throughout history. This subject has received increasing attention in the field of traditional medicine in recent years. Tragopogon reticulatus, as a forage grass plant in turkey, is a species that stands out with rich vitamin and mineral content. The aim of this study was to investigate the antimicrobial, antioxidant, and cytotoxic activities of T. reticulatus. Two separate extracts were prepared at different concentrations using hexane and methanol solvents obtained from the T. reticulatus plant, and cytotoxic activity was evaluated by the MTT Assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) method on human liver cancer (HepG2) and human breast cancer (MCF7) cell lines. The antioxidant activity of the extracts obtained from methanol and hexane was determined by the DPPH (2,2-diphenyl 1-picrylhydrazyl) radical scavenging capacity method. Antimicrobial activity was evaluated by the disk diffusion method, and bacteria such as Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, Bacillus megaterium, and Candida albicans fungus were used in the study. The hexane extract obtained from the T. reticulatus plant showed a high cytotoxic activity of 44% in the MCF7 cell line, while it showed a 51% dead cell count in the HepG2 cell line. When the antioxidant results were evaluated, it was determined that the DPPH radical scavenging effect of the hexane extract exhibited the most effective activity at 19%. When the antimicrobial results were evaluated, it was determined that the concentrations prepared with hexane exhibited the most effective activity with a zone diameter of 16 mm against B. megaterium. These findings suggest that T. reticulatus plant extracts can be considered as a potential source. These results may shed light on future research into the biological effects of the plant and contribute to the development of new treatment methods.

Plasma‐Assisted Preparation and Properties of Chitosan‐Based Magnetic Hydrogels

Chitosan‐based magnetic hydrogel has become a research hotspot in the field of material science due to the driven deformation or movement under the action of magnetic field. The unique properties make it show great application potential in medicine, biosensing, environmental protection, and other fields. Herein, the chitosan‐based nanomagnetic composite hydrogels are prepared by plasma assistance. The surface morphology, infrared spectra, mechanical properties, swelling ratio, water absorption, porosity, and degradation of the prepared hydrogels are investigated. The results prove the superiority of plasma‐assisted preparation of magnetic chitosan hydrogels and provide a new method for the design of smart hydrogels.

Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach

Over the past year, there has been a significant rise in interest in the application of open-source artificial intelligence models (OSAIM) in the field of medicine. An increasing number of studies focus on evaluating the capabilities of these models in image analysis, including magnetic resonance imaging (MRI). This study aimed to investigate whether two of the most popular open-source AI models, namely ChatGPT 4o and Gemini Pro, can analyze MRI video sequences with single-phase contrast in sagittal and frontal projections, depicting a posterior fossa tumor corresponding to a medulloblastoma in a child. The study utilized video files from single-phase contrast-enhanced head MRI in two planes (frontal and sagittal) of a child diagnosed with a posterior fossa tumor, type medulloblastoma, confirmed by histopathological examination. Each model was separately provided with the video file, first in the sagittal plane, analyzing three different sets of commands from the most general to the most specific. The same procedure was applied to the video file in the frontal plane. The Gemini Pro model did not conduct a detailed analysis of the pathological change but correctly identified the content of the video file, indicating it was a brain MRI, and suggested that a specialist in the field should perform the evaluation. Conversely, ChatGPT 4o conducted image analysis but failed to recognize that the content was MRI. The attempts to detect the lesion were random and varied depending on the plane. These models could not accurately identify the video content or indicate the area of the neoplastic change, even after applying detailed queries. The results suggest that despite their widespread use in various fields, these models require further improvements and specialized training to effectively support medical diagnostics.

Leveraging the Physicochemical Attributes of Biomimetic Hydrogel Nanocomposites in Stem Cell Differentiation.

The field of tissue engineering has witnessed significant advancements with the advent of hydrogel nanocomposites (HNC), emerging as a highly promising platform for regenerative medicine. HNCs provide a versatile platform that significantly enhances the differentiation of stem cells into specific cell lineages, making them highly suitable for tissue engineering applications. By incorporating nanoparticles, the mechanical properties of hydrogels, such as elasticity, porosity, and stiffness, are improved, addressing common challenges such as short-term stability, cytotoxicity, and scalability. These nanocomposites also exhibit enhanced biocompatibility and bioavailability, which are crucial to their effectiveness in clinical applications. Furthermore, HNCs are responsive to various triggers, allowing for precise control over their chemical properties, which is beneficial in creating 3D microenvironments, promoting wound healing, and enabling controlled drug delivery systems. This review provides a comprehensive overview of the production methods of HNCs and the factors influencing their physicochemical and biological properties, particularly in relation to stem cell differentiation and tissue repair. Additionally, it discusses the challenges in developing HNCs and highlights their potential to transform the field of regenerative medicine through improved mechanotransduction and controlled release systems.

Structure-activity relationships and activity enhancement techniques of marine bioactive peptides (MBPs).

Marine bioactive peptides (MBPs) are a type of natural compound with a variety of bioactivities, such as anticancer, antimicrobial, antioxidant, and antihypertensive. Due to a wide range of sources, low toxicity, and high specificity, MBPs have now received extensive attention in the fields of food, medicine, and cosmetics. The structure of MBPs determines their biological activities. Therefore, it is essential to analyze the relationship between the structure and bioactivity of MBPs. Because of the advantages of mild conditions, high specificity, safety, and environmental friendliness, enzymatic hydrolysis has become the most commonly used method to produce MBPs. However, the high cost and low yield of enzymatic methods have motivated researchers to search for alternative technologies. Novel pretreatments like ultrasound, microwave, high hydrostatic pressure, and pulsed electric fields have been employed in the production of MBPs. By inducing protein unfolding and increasing enzymatic cleavage sites, these techniques have been demonstrated to accelerate protein hydrolysis and enhance the biological activity of MBPs. This article reviews recent research advances on marine-derived protein hydrolysates and peptides, discusses the relationship between their biological activity and structure, and compares the mechanisms of action of different novel technologies used to promote protein hydrolysis and enhance the biological activity of MBPs. In addition, the current challenges facing the development and application of MBPs are outlined and possible future work in tackling these challenges is also suggested in the current review. It is hoped that this review can promote further development and application of marine active substances.

Cryoablation in Chronic Pain Management: A Narrative Review

Abstract Cryoneurolysis is a technique of applying extreme cold temperatures to specific sensory nerves, causing a prolonged period of loss of conduction through that nerve. This technique has found widespread application in the field of interventional pain medicine in a variety of conditions including chronic postsurgical pain, phantom limb pain (PLP), and cancer pain. Its use in chronic pain states has been aided further by the advent of handheld cryoneurolysis devices and the widespread use of ultrasonographic imaging to guide and target nerves accurately. Chronic pain states – chronic knee pain from osteoarthritis, refractory PLP, temporomandibular disorders, cervicogenic headaches, low back pain, different peripheral neuropathies, and metastatic bone tumor pain – are some of the conditions where cryoneurolysis has been proven to benefit patients in cases where the symptoms were refractory to other conventionally used modalities. Chronic pain is a complex and multifaceted condition and hence requires a variety of individualized treatment approaches. Current research on this treatment method for chronic pain indicates that it carries low risk and, when used correctly, offers extended pain relief without causing motor function loss. This narrative review includes all types of scientific studies, published between 2010 and 2023, which were selected using a specific set of keywords through two different literature search engines. In conclusion, it explains how cryoneurolysis works to provide long-term pain relief and summarizes the latest evidence supporting its use in chronic pain management.

Developments in Transfusion Medicine: Pulmonary Transfusion Reactions and Novel Blood Cell Labeling Techniques.

Staying updated on advancements in transfusion medicine is crucial, especially in critical care and perioperative setting, where timely and accurate transfusions can be lifesaving therapeutic interventions. This narrative review explores the landscape of transfusion-related adverse events, focusing on pulmonary transfusion reactions such as transfusion-associated circulatory overload (TACO) and transfusion-related acute lung injury (TRALI). TACO and TRALI are the leading causes of transfusion-related morbidity and mortality; however, specific treatments are lacking. Understanding the current incidence, diagnostic criteria, pathogenesis, treatment, and prevention strategies can equip clinicians to help reduce the incidence of these life-threatening complications. The review discusses emerging pathogenic mechanisms, including the possible role of inflammation in TACO and the mechanisms of reverse TRALI and therapeutic targets for TACO and TRALI, emphasizing the need for further research to uncover preventive and treatment modalities. Despite advancements, significant gaps remain in our understanding of what occurs during transfusions, highlighting the necessity for improved monitoring methods. To address this, the review also presents novel blood cell labeling techniques in transfusion medicine used for improving monitoring, quality assessment, and as a consequence, potentially reducing transfusion-related complications. This article aims to provide an update for anesthesiologists, critical care specialists, and transfusion medicine professionals regarding recent advancements and developments in the field of transfusion medicine.

STUDYING INDICATORS OF CIRCADIAN RHYTHMS OF MACROELEMENTS CONCENTRATION IN HUMAN SALIVA UNDER NORMAL CONDITIONS

Seasonal and circadian research in the field of chronobiology and chronomedicine is currently of particular interest to scientists. Chronobiology has discovered previously unknown approaches to the diagnosis, treatment and prevention of diseases. For biological objects, as an open thermodynamic system, in their development have absorbed all the laws of the rhythmically oscillating external world and interact with it with the help of these vibrations. Therefore, at the present stage of the development of chronobiology, any research that fills, like bricks, still empty niches of new knowledge reflects the advanced frontiers of natural science and is included in the creation of a future general theory of biology. This is confirmed by a number of discoveries of scientists in the field of physics, physiology and medicine. Thus, a group of scientists became laureates of the Nobel Prize in physics in 2017 for the discovery of gravitational waves, and in the field of physiology and medicine, a number of scientists (Hall, Rosbash, Young) became Nobel laureates for a series of works on the topic: "Studying the mechanisms of the circadian dynamics of the body." Today, chronobiology can characterize periodic fluctuations of, perhaps, all organs, all body systems. A large amount of data has been accumulated on the daily and seasonal rhythms of the digestive and urinary tract organs, respiratory and cardiovascular systems, brain, etc. A violation of biorhythms can lead to the development of defects in oral and maxillofacial tissues, the development of inflammation and tumors, which affects cell proliferation, apoptosis, oxidative stress, etc. The main purpose of this work was to study the characteristics of circadian rhythms of macroelement concentrations in human saliva under normal conditions. A feature of the research work is the determination of the concentration of macroelements in saliva in the summer. The saliva of 12 healthy volunteers was taken as the study material. Age 22-30 years. The method of spectrophotometry was used in this research work. The research work was carried out in the summer at the Department of Biophysics, Biomedicine and Neuroscience of the al-Farabi Kazakh National University and at the City Clinical Hospital №1 in Almaty. During the study, a saliva sample was collected on 1.5 ml of Eppendorf. All samples were stored in the freezer. Before the experiment, the material was soaked at room temperature for 30-40 minutes. Using the spectrophotometric method, we determined the concentration of macroelements in saliva using a set of special reagents. We have identified the features of the daily dynamics of the concentration of macroelements in the saliva of healthy people in summer: K, Na, Ca, Mg and P. The concentration of macroelements varied widely throughout the day. As a result of the study, the concentration index of K during the day normally ranges from 528±36 µg/ml to 634±42 µg/ml; Na - from 153.4±19.8 µg/ml to 144.9±25.1 µg/ml; Ca-from 35.4±4.0 µg/ml to 45.5±5.1 µg/ml; Mg-from 2.12±0.15 µg/ml to 4.02±0.25 µg/ml and P-from 134.5±7.7 µg/ml to 151.6±10.1 µg/ml. Summing up, we have identified the features of the daily dynamics of the concentration of macroelements in the saliva of healthy people in summer. In summer, the daily dynamics of the concentration of macroelements in saliva ranged from 2.12±0.15 µg/ml (Mg) to 634±12 µg/ml (K).

Беременность как социально значимое состояние

The article is devoted to the consideration of a woman's pregnancy status in the context of the grounds and prospects of social protection and social security. Based on the results of the analysis of the current sectoral legal regulation and based on the results of research in the field of medicine, psychology, and sociology, it is proposed to understand pregnancy as an independent fact-the basis of social security. This proposal is formulated on the basis of finding the process and result of pregnancy, its well-being in the field of both individual and public interests.

The Clinical Utility of Large Language Models in Diagnosing Neurocognitive Disorders among NACC Participants

Abstract Objective Our study aimed to investigate ChatGPT’s ability to diagnose neurocognitive disorders in older adults based on neuropsychological interviews and test results and compare its accuracy with neuropsychologist consensus diagnosis. Methods We prompted ChatGPT-4 to provide provisional diagnoses (normal changes, mild cognitive impairment, or dementia) for each case. We also asked ChatGPT to diagnose the underlying etiology. We evaluated interrater reliability (IRR) with Cohen’s kappa and a chi-square test. Receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) values were computed to explore measures predicting diagnostic agreement. Results Participants were 15,048 older adults (Mage = 69.13, 56.7% female) assessed in Alzheimer’s Disease Core Centers across the United States. For cognitive diagnoses, Cohen’s kappa demonstrated fair agreement (κ = 0.42; p &amp;lt; 0.001). The chi-square test [χ2 (6) = 7896.51, p &amp;lt; 0.001] revealed a significant and strong association (Cramer’s V = 0.51, p &amp;lt; 0.001) between ChatGPT and clinicians’ diagnoses. For the underlying etiology, Cohen’s kappa demonstrated fair agreement on specific diagnoses (κ = 0.38; p &amp;lt; 0.001). The chi-square test [χ2 (24) = 5353.04, p &amp;lt; 0.001] revealed a significant and strong association (Cramer’s V = 0.30, p &amp;lt; 0.001) between ChatGPT and clinicians’ diagnoses. ROC curves demonstrate that no variables predicted diagnostic agreement at above chance. Conclusion Our findings demonstrate the potential clinical utility of integrating LLMs into neuropsychology. While significant IRR was found between ChatGPT and the neuropsychologist diagnosis, further research is needed prior to the adoption of LLMs into clinical practice, given ongoing developments of artificial intelligence in the field of medicine.

Green synthesized silver nanoparticles and their biomedical applications: a review

Over the last two decades, scientists have become increasingly interested in metallic nanoparticles due to their unique properties, especially in the biomedical field. Green synthesis of metal nanoparticles is a rapidly growing field in nanotechnology due to its viability, reduced toxicity, ecofriendly nature, and ease of preparation. Plants have long been a centre of attraction in the field of medicine because they are a unique source of a variety of bioactive compounds. Silver has been known for its medicinal properties, especially its antibacterial therapeutic effect. Among other metal nanoparticles, silver nanoparticles have taken a special place since it has made a big impact on medical applications. This review aims to provide a comprehensive understanding of the biomedical qualities of medicinal plants, as well as an overview of their impact on the synthesis of green silver nanoparticles. Various biomedical applications of plant-mediated silver nanoparticles will also be highlighted.

A - 133 The Clinical Utility of Large Language Models in Diagnosing Neurocognitive Disorders among NACC Participants

Abstract Objective Our study aimed to investigate ChatGPT’s ability to diagnose neurocognitive disorders in older adults based on neuropsychological interviews and test results and compare its accuracy with neuropsychologist consensus diagnosis. Methods We prompted ChatGPT-4 to provide provisional diagnoses (normal changes, mild cognitive impairment, or dementia) for each case. We also asked ChatGPT to diagnose the underlying etiology. We evaluated interrater reliability (IRR) with Cohen’s kappa and a chi-square test. Receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) values were computed to explore measures predicting diagnostic agreement. Results Participants were 15,048 older adults (Mage = 69.13, 56.7% female) assessed in Alzheimer’s Disease Core Centers across the United States. For cognitive diagnoses, Cohen’s kappa demonstrated fair agreement (κ = 0.42; p &amp;lt; 0.001). The chi-square test [χ2 (6) = 7896.51, p &amp;lt; 0.001] revealed a significant and strong association (Cramer’s V = 0.51, p &amp;lt; 0.001) between ChatGPT and clinicians’ diagnoses. For the underlying etiology, Cohen’s kappa demonstrated fair agreement on specific diagnoses (κ = 0.38; p &amp;lt; 0.001). The chi-square test [χ2 (24) = 5353.04, p &amp;lt; 0.001] revealed a significant and strong association (Cramer’s V = 0.30, p &amp;lt; 0.001) between ChatGPT and clinicians’ diagnoses. ROC curves demonstrate that no variables predicted diagnostic agreement at above chance. Conclusion Our findings demonstrate the potential clinical utility of integrating LLMs into neuropsychology. While significant IRR was found between ChatGPT and the neuropsychologist diagnosis, further research is needed prior to the adoption of LLMs into clinical practice, given ongoing developments of artificial intelligence in the field of medicine.

Identification of a transglutaminase from Bacillus amyloliquefaciens: Gene mining, protein expression, mechanism analysis and enzymatic characterization

Transglutaminase (TGase) can catalyze the crosslinking within or between protein molecules, improving the structural and functional properties of proteins. It has been widely used in artificial meat, yogurt products, flour products and other foods. In this study, a high yield TGase strain was screened from fermented soybean, and it was identified as the Bacillus amyloliquefaciens XCT-09. Then, the transglutaminase (X9Tgl) from XCT-09 was expressed in Escherichia coli BL21(DE3), and the enzyme activity of purified X9Tgl reached 5.73 U/mg. Subsequently, the catalytic mechanism of X9Tgl was elucidated, and the X9Tgl contained the active center of E115, C116 and E187, as well as the substrate binding sites W149 and F69. Furthermore, this study characterized the enzymatic properties of X9Tgl, and the optimal catalytic temperature and pH were 60 °C and 6, respectively. Moreover, X9Tgl exhibited stronger thermal stability and pH tolerance than STG, and it was not inhibited by the majority of metal ions. This study successfully identified a X9Tgl with high thermal stability, and pH tolerance, which provided a potential TGase resource for application in the food and medicine fields.