Assessing question characteristic influences on ChatGPT's performance and response-explanation consistency: Insights from Taiwan's Nursing Licensing Exam.

BACKGROUND: Investigates the integration of an artificial intelligence tool, specifically ChatGPT, in nursing education, addressing its effectiveness in exam preparation and self-assessment. OBJECTIVE: This study aims to evaluate the performance of ChatGPT, one of the most promising artificial intelligence-driven linguistic understanding tools in answering question banks for nursing licensing examination preparation. It further analyzes question characteristics that might impact the accuracy of ChatGPT-generated answers and examines its reliability through human expert reviews. DESIGN: Cross-sectional survey comparing ChatGPT-generated answers and their explanations. SETTING: 400 questions from Taiwan's 2022 Nursing Licensing Exam. METHODS: The study analyzed 400 questions from five distinct subjects of Taiwan's 2022 Nursing Licensing Exam using the ChatGPT model which provided answers and in-depth explanations for each question. The impact of various question characteristics, such as type and cognitive level, on the accuracy of the ChatGPT-generated responses was assessed using logistic regression analysis. Additionally, human experts evaluated the explanations for each question, comparing them with the ChatGPT-generated answers to determine consistency. RESULTS: ChatGPT exhibited overall accuracy at 80.75 % for Taiwan's National Nursing Exam, which passes the exam. The accuracy of ChatGPT-generated answers diverged significantly across test subjects, demonstrating a hierarchy ranging from General Medicine at 88.75 %, Medical-Surgical Nursing at 80.0 %, Psychology and Community Nursing at 70.0 %, Obstetrics and Gynecology Nursing at 67.5 %, down to Basic Nursing at 63.0 %. ChatGPT had a higher probability of eliciting incorrect responses for questions with certain characteristics, notably those with clinical vignettes [odds ratio 2.19, 95 % confidence interval 1.24-3.87, P = 0.007] and complex multiple-choice questions [odds ratio 2.37, 95 % confidence interval 1.00-5.60, P = 0.049]. Furthermore, 14.25 % of ChatGPT-generated answers were inconsistent with their explanations, leading to a reduction in the overall accuracy to 74 %. CONCLUSIONS: This study reveals the ChatGPT's capabilities and limitations in nursing exam preparation, underscoring its potential as an auxiliary educational tool. It highlights the model's varied performance across different question types and notable inconsistencies between its answers and explanations. The study contributes significantly to the understanding of artificial intelligence in learning environments, guiding the future development of more effective and reliable artificial intelligence-based educational technologies. TWEETABLE ABSTRACT: New study reveals ChatGPT's potential and challenges in nursing education: Achieves 80.75 % accuracy in exam prep but faces hurdles with complex questions and logical consistency. #AIinNursing #AIinEducation #NursingExams #ChatGPT.

Label-free Super-resolution Imaging Enabled by Vibrational Imaging of Swelled Tissue and Analysis.

The universal utilization of fluorescence microscopy, especially super-resolution microscopy, has greatly advanced knowledge about modern biology. Conversely, the requirement of fluorophore labeling in fluorescent techniques poses significant challenges, such as photobleaching and non-uniform labeling of fluorescent probes and prolonged sample processing. In this protocol, the detailed working procedures of vibrational imaging of swelled tissue and analysis (VISTA) are presented. VISTA circumvents obstacles associated with fluorophores and achieves label-free super-resolution volumetric imaging in biological samples with spatial resolution down to 78 nm. The procedure is established by embedding cells and tissues in hydrogel, isotropically expanding the hydrogel sample hybrid, and visualizing endogenous protein distributions by vibrational imaging with stimulated Raman scattering microscopy. The method is demonstrated on both cells and mouse brain tissues. Highly correlative VISTA and immunofluorescence images were observed, validating the protein origin of imaging specificities. Exploiting such correlation, a machine learning-based image-segmentation algorithm was trained to achieve multi-component prediction of nuclei, blood vessels, neuronal cells, and dendrites from label-free mouse brain images. The procedure was further adapted to investigate pathological poly-glutamine (polyQ) aggregates in cells and amyloid-beta (Aß) plaques in brain tissues with high throughput, justifying its potential for large-scale clinical samples.

Super-resolution label-free volumetric vibrational imaging.

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 78 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

High spatial-resolution imaging of label-free in vivo protein aggregates by VISTA.

Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer's disease and Huntington's disease. High-resolution holistic mapping of the fine structures from these aggregates should facilitate our understanding of their pathological roles. Here, we achieved label-free high-resolution imaging of the polyQ and the amyloid-beta (Aß) aggregates in cells and tissues utilizing a sample-expansion stimulated Raman strategy. We further focused on characterizing the Aß plaques in 5XFAD mouse brain tissues. 3D volumetric imaging enabled visualization of the whole plaques, resolving both the fine protein filaments and the surrounding components. Coupling our expanded label-free Raman imaging with machine learning, we obtained specific segmentation of aggregate cores, peripheral filaments together with cell nuclei and blood vessels by pre-trained convolutional neural network models. Combining with 2-channel fluorescence imaging, we achieved a 6-color holistic view of the same sample. This ability for precise and multiplex high-resolution imaging of the protein aggregates and their micro-environment without the requirement of labeling would open new biomedical applications.

Remodeling nanoDESI Platform with Ion Mobility Spectrometry to Expand Protein Coverage in Cancerous Tissue.

Nanospray desorption electrospray ionization mass spectrometry is an ambient ionization technique that is capable of mapping proteins in tissue sections. However, high-abundant molecules or isobaric interference in biological samples hampers its broad applications in probing low-abundant proteins. To address this challenge, herein we demonstrated an integrated module that coupled pneumatic-assisted nanospray desorption electrospray ionization mass spectrometry with high-field asymmetric ion mobility spectrometry. Using this module to analyze mouse brain sections, the protein coverage was significantly increased. This improvement allowed the mapping of low-abundant proteins in tissue sections with a 5 µm spatial resolution enabled by computationally assisted fusion with optical microscopic images. Moreover, the module was successfully applied to characterize melanoma in skin tissues based on the enhanced protein profiles. The results suggested that this integrating module will be potentially applied to discover novel proteins in cancers.

Toward the Rational Design of Universal Dual Polarity Matrix for MALDI Mass Spectrometry.

A series of novel anthranilic acid derivatives I-IV, of which COOH-NH2 (I) and COOH-NHMe (IV) are endowed with acid and base bifunctionality, were designed and synthesized for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry applications in dual polarity molecular imaging of biological samples, particularly for lipids. The heat of protonation, deprotonation, and proton transfer reaction as well as the capability of analyzing biomolecules in both positive and negative ion modes for I-IV were systematically investigated under standard 355 nm laser excitation. The results indicate correlation between dual polarity and acid-base property. Further, COOH-NHMe (IV) showed a unique performance and was successfully applied as the matrix for MALDI-TOF mass spectrometry imaging (MSI) for studying the mouse brain. Our results demonstrate the superiority of COOH-NHMe (IV) in detecting more lipid and protein species compared to commercially available matrices. Moreover, MALDI-TOF MSI results were obtained for lipid distributions, making COOH-NHMe (IV) a potential next generation universal matrix.

Precision biomarker discovery powered by microscopy image fusion-assisted high spatial resolution ambient ionization mass spectrometry imaging.

Mass spectrometry imaging (MSI) using the ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry were employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion were verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-µm spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI was ∼100 µm. In this regard, sharpened molecular histology of tissue sections was achieved, providing complementary references to the pathology. Such a multi-modal integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen potential biomarkers on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section were identified.

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aß) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 µm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 µm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.

Finely Tunable Surface Wettability by Two-Dimensional Molecular Manipulation.

Local molecular environment governs material interface properties, especially the substrate's exposing behavior and overall functionality expression. Although current techniques can provide efficient surface property modification, challenges in molecule spatial distribution and composition controls limited the generation of homogeneous and finely tunable molecular environment. In this study, Au-thiolate rupturing operation in chemical lift-off lithography (CLL) is used to manipulate the substrate interface molecular environment. The creation of randomly distributed artificial self-assembled monolayer defects generates vacancies for substrate property modification through back-insertion of molecules with opposite functionalities. Surface wettability adjustment is utilized as an example, where well-controllable molecule distribution provides finely tunable substrate affinity toward liquids with different physical properties. The distinct property difference between two surface regions assists microdroplet formation when liquids flow through, not only water solution but also low-surface-tension organic liquids. These microdroplet arrays become a template to guide material assembly in its formation process and act as pH-sensitive platforms for high-throughput detection. Furthermore, the tunability of the molecular pattern in this approach helps minimize the coffee-ring effect and the sweet-spot issue in matrix-assisted laser desorption/ionization mass spectrometry. Two-dimensional molecular manipulation in the CLL operation, therefore, holds the capability toward controlling homogeneous material surface property and toward exhibiting behavior adjustments.

Cross-Linked Fluorescent Supramolecular Nanoparticles for Intradermal Controlled Release of Antifungal Drug-A Therapeutic Approach for Onychomycosis.

The existing approaches to onychomycosis demonstrate limited success since the commonly used oral administration and topical cream only achieve temporary effective drug concentration at the fungal infection sites. An ideal therapeutic approach for onychomycosis should have (i) the ability to introduce antifungal drugs directly to the infected sites; (ii) finite intradermal sustainable release to maintain effective drug levels over prolonged time; (iii) a reporter system for monitoring maintenance of drug level; and (iv) minimum level of inflammatory responses at or around the fungal infection sites. To meet these expectations, we introduced ketoconazole-encapsulated cross-linked fluorescent supramolecular nanoparticles (KTZ⊂c-FSMNPs) as an intradermal controlled release solution for treating onychomycosis. A two-step synthetic approach was adopted to prepare a variety of KTZ⊂c-FSMNPs. Initial characterization revealed that 4800 nm KTZ⊂c-FSMNPs exhibited high KTZ encapsulation efficiency/capacity, optimal fluorescent property, and sustained KTZ release profile. Subsequently, 4800 nm KTZ⊂c-FSMNPs were chosen for in vivo studies using a mouse model, wherein the KTZ⊂c-FSMNPs were deposited intradermally via tattoo. The results obtained from (i) in vivo fluorescence imaging, (ii) high-performance liquid chromatography quantification of residual KTZ, (iii) matrix-assisted laser desorption/ionization mass spectrometry imaging mapping of KTZ distribution in intradermal regions around the tattoo site, and (iv) histology for assessment of local inflammatory responses and biocompatibility, suggest that 4800 nm KTZ⊂c-FSMNPs can serve as an effective treatment for onychomycosis.

Effectiveness of Needs-oriented Hospital Discharge Planning for Caregivers of Patients With Schizophrenia.

Hospital discharge planning for clients with schizophrenia reduces client rehospitalization rates and improves their medication adherence. The effectiveness of caregiver participation in hospital discharge planning has seldom been explored. The purpose of this study was to examine the effectiveness of caregiver participation in hospital discharge planning for clients with schizophrenia in reducing caregiver burden and improving health status. A quasi-experimental research design was adopted. The research location was in a psychiatric hospital in Northern Taiwan. The target population was caregivers of inpatients with schizophrenia. Nurses served as care coordinators and provided six-step hospital discharge planning services to caregivers. Structured questionnaires were employed to measure caregiver burden and health status. Intervention effect was tested using analysis of covariance in which outcome measure at pretest and selected demographic variables were treated as covariates. A total of 114 caregivers completed pretest and posttest evaluations, with 57 people in each group. A significant difference was found between the experimental and the control group regarding the caregiver burden and health status (P<0.001) The caregiver burden and health status of the experimental group improved more significantly compared with the control group. The caregiver-involved discharge planning process developed in this study effectively reduced the burden placed on caregivers and improved their health status. Mental health nurses can serve as the main care coordinators for assessment, planning, referral and provision of the required services. Caregiver-involved hospital discharge planning should become part of the routine care process.

A Simple Sonication Improves Protein Signal in Matrix-Assisted Laser Desorption Ionization Imaging.

Proper matrix application is crucial in obtaining high quality matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI). Solvent-free sublimation was essentially introduced as an approach of homogeneous coating that gives small crystal size of the organic matrix. However, sublimation has lower extraction efficiency of analytes. Here, we present that a simple sonication step after the hydration in standard sublimation protocol significantly enhances the sensitivity of MALDI MSI. This modified procedure uses a common laboratory ultrasonicator to immobilize the analytes from tissue sections without noticeable delocalization. Improved imaging quality with additional peaks above 10 kDa in the spectra was thus obtained upon sonication treatment. Graphical Abstract ᅟ.