Stand-alone Tool Research Articles

The Multiple Errands Test-Home Version and Its Association With Driving Potential: A Pilot Study.

Driving is a complex occupation requiring the interplay of high-level cognitive, physical, sensory, and behavioral skills for safe performance. Occupational therapists need to routinely address driving with adults as an occupational performance area. Further research is needed to determine whether performance-based assessment tools can support occupational therapists in screening client driving potential. To conduct a pilot study to determine whether the Multiple Errands Test-Home Version (MET-Home), as a performance-based assessment, either alone or in combination with other assessments, should be further investigated for use by occupational therapists to screen clients' driving potential. Cross-sectional pilot study. Private in-clinic and community setting, including participants' homes. Convenience sampling recruited 28 participants through private occupational therapy driver assessors. Participants underwent a comprehensive in-clinic and behind-the-wheel assessment, as per standard practice, and three additional cognitive tests. Data were summarized with descriptive statistics, and univariate analyses were used to examine the relationships between cognitive assessment scores and driving outcomes. The MET-Home, as a stand-alone tool and in combination with other cognitive assessment scores, was not associated with driving outcomes (pass-fail). However, participant self-assessment of their MET-Home performance was associated with driving outcomes (pass-fail; p = .014). Although our findings suggest that the MET-Home is unlikely to screen for driving potential, further research of performance-based assessment tool use by occupational therapists is needed to support identification of the optimal type and timing of client referral for comprehensive assessment. Plain-Language Summary: The Multiple Errands Test-Home Version (MET-Home) is commonly completed by occupational therapists. Although this pilot study revealed that MET-Home scores were not associated with driving outcomes, performance-based assessments such as the MET-Home have the potential to guide occupational therapists when screening clients to determine when further, comprehensive assessment is indicated.

The feasibility of integrating an alcohol screening clinical decision support tool into primary care clinical software: a review and Australian key stakeholder study

BackgroundThis study explored the feasibility of integrating a clinical decision support tool into general practice clinical management software in Australia to prompt for alcohol screening among patients who are pregnant or planning a pregnancy. The study aimed to increase understanding of what is an appropriate and acceptable clinical decision support tool, the circumstances when a prompt to use such a screening tool should occur, and the barriers and enablers of successful implementation.MethodsThis feasibility study employed a mixed methods approach and purposive sampling to identify key stakeholders to interview. Participants included vendors of clinical software used in Australian general practice, clinicians in general practice, and relevant others. Data from a literature review and 23 interviews were analysed leading to recommendations which were ‘sense-tested’ by an additional 22 stakeholders.ResultsAlthough there are at least 18 clinical software packages used in Australian general practice, it is feasible to integrate an alcohol screening tool for pregnancy into software for the majority of general practices in Australia. The AUDIT-C alcohol screening tool for pregnancy was widely accepted as suitable for such a purpose. Clinicians suggested the greatest barriers to screening were lack of time within antenatal consultations and insufficient remuneration for longer consultations. Many clinicians saw opportunity for introducing a multifunctional antenatal tool that could incorporate screening and clinical decision support for alcohol, tobacco and other substance use, mental health, domestic and family violence and potentially other areas informing healthy pregnancy. It could also be used opportunistically for preconception screening and counselling. Deployment of the tool could be supported by an education campaign from professional associations.ConclusionThe integration of a tool for screening for alcohol use among women who are pregnant or planning pregnancy into general practice clinical software is feasible; however, a multifunctional antenatal screening tool, incorporating other psychosocial elements, was considered more useful than a stand-alone alcohol screening tool. Codesign is needed with vendors and end-users to develop an acceptable tool that can be widely implemented. Issues with GP renumeration need also to be addressed to encourage alcohol screening pre-pregnancy and in the early months of pregnancy.

The use of generative artificial intelligence in surgical education: a narrative review

The introduction of generative artificial intelligence (AI) has revolutionized healthcare and education. These AI systems, trained on vast datasets using advanced machine learning (ML) techniques and large language models (LLMs), can generate text, images, and videos, offering new avenues for enhancing surgical education. Their ability to produce interactive learning resources, procedural guidance, and feedback post-virtual simulations makes them valuable in educating surgical trainees. However, technical challenges such as data quality issues, inaccuracies, and uncertainties around model interpretability remain barriers to widespread adoption. This review explores the integration of generative AI into surgical training, assessing its potential to enhance learning and teaching methodologies. While generative AI has demonstrated promise for improving surgical education, its integration must be approached cautiously, ensuring AI input is balanced with traditional supervision and mentorship from experienced surgeons. Given that generative AI models are not yet suitable as standalone tools, a blended learning approach that integrates AI capabilities with conventional educational strategies should be adopted. The review also addresses limitations and challenges, emphasizing the need for more robust research on different AI models and their applications across various surgical subspecialties. The lack of standardized frameworks and tools to assess the quality of AI outputs in surgical education necessitates rigorous oversight to ensure accuracy and reliability in training settings. By evaluating the current state of generative AI in surgical education, this narrative review highlights the potential for future innovation and research, encouraging ongoing exploration of AI in enhancing surgical education and training.

Conservative taxonomy and quality assessment of giant virus genomes with GVClass

Large double-stranded DNA viruses of the phylum Nucleocytoviricota (Giant Viruses; GVs) are the largest known viruses, infecting various eukaryotic hosts, particularly protists and algae. These viruses impact biogeochemical cycles and host genome evolution but are challenging to identify and classify due to their complex genomes. We present GVClass, a tool for identifying giant viruses in sequence data, providing taxonomic assignments, and estimating genome completeness and contamination. GVClass employs optimized gene calling and a conservative approach using consensus single-protein phylogenies for robust taxonomic classification, relying on highly conserved orthologous groups. Benchmarking demonstrates over 90% accuracy at the genus-level and >99% at higher taxonomic ranks. GVClass addresses classification challenges and is available as a standalone tool and integrated into the Integrated Microbial Genomes/Virus database (IMG/VR).

Validation process against late phase conditions of the passive autocatalytic recombiner simulation code PARUPM as a standalone tool using experimental data from REKO-3 and THAI facilities

In case of a nuclear accident with core damage in a light water reactor, the oxidation of the fuel cladding and other materials could lead to the release of combustible gases (H2 and CO) to the containment building. To mitigate the potential risk of combustion of these gases, passive autocatalytic recombiners (PARs) have been installed in numerous nuclear reactors in Europe and worldwide. PARs recombine H2 and CO with O2 producing H2O and CO2, respectively, without an open flame.PARUPM is a code that simulates the behaviour of PARs using a physicochemical model approach. In the framework of the AMHYCO project (EU-funded Horizon 2020 project), which seeks to advance the understanding and simulation capabilities to support the combustion risk management in severe accidents, the code has been extensively enhanced and developed to simulate PAR operation with H2/CO/O2/steam mixtures. Alongside these new capabilities, the code needed a new validation process.In this paper, the process of validation of PARUPM as a standalone code is described. The validation for steady state conditions was achieved through comparison with REKO-3 experimental data while the transient conditions were compared with results obtained with the THAI test facility. A thorough analysis of the code capabilities was performed by comparing the numerical results with experimental data for a broad series of conditions, namely: a range of different input gas temperatures and concentrations, oxygen starvation, CO poisoning, etc.

A comparison of assessment tools for childcare centers in high vs. low resource settings.

Studies suggest issues may arise when using childcare setting assessment tools designed for high-resource settings in low-resource settings to assess and improve the quality of care, including placing disproportionate weight on features of the childcare environment that may not be available or culturally appropriate within the low-resource context. This study compares a novel assessment tool developed in and for low-income and low-resource settings with a standardized "gold standard" tool developed for use in high-resource settings. The study included a randomized sample of 34 childcare centers in a low-resource context that provided care for approximately 918. Results suggest that the WCI-QCUALS assessment tool performed better at differentiating among childcare settings that were consolidated into the lowest rating on the ECERS-R. Further, the WCI-QCUALS was found to be a feasible, appropriate stand-alone tool for assessing the quality of childcare centers in low-resource settings. Additional refinement and validity testing in other countries and contexts will improve the understanding of how the quality of childcare across different contexts can be measured, and improved assessment of childcare quality in low-resource settings will enhance the ability to identify low-quality care that can be remedied to ensure appropriate care for vulnerable children.

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray

PurposePneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists. MethodWe retrospectively ran the AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports. ResultsOverall, the AI system achieved an accuracy of 74 % (95%CI 68–79 %), with a sensitivity of 66 % (95%CI 59–73 %) and a specificity of 93 % (95%CI 85–97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71–82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66–79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75–91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040). ConclusionsThe proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.

Frustration Gap Bridging: A Second Look at Vocabulary Proficiency with the Yes/No Test

This research advances the scrutiny of the Yes/No test's (Meara: 1990) efficacy in evaluating vocabulary proficiency among language learners of high, middle, and low proficiency levels, focusing on its capability to mitigate the frustration commonly encountered by this demographic. As highlighted in the recent investigation (Goetz, 2023), the Yes/No test prompted respondents to signify their word familiarity through binary choices. Yes, they know a word, and No, they don’t. Involving a paltry 56 English language learners of lower proficiency to determine if the test's impact on enhancing vocabulary skills had merit, it was found that contrary to the anticipated outcomes, the test did not yield significant advancements in students' performance on subsequent unit tests compared to their counterparts who were not subjected to the Yes/No test. This absence of tangible benefits, combined with the test's failure to deliver a contextualized and nuanced comprehension of vocabulary, raises questions regarding its efficacy as a pedagogical tool for vocabulary enhancement. Nevertheless, the results did not dismiss the Yes/No test's potential utility. For this study, a second look will address two critical shortcomings: the limited sample size and the absence of a diverse range of proficiency levels among participants. This inquiry reevaluates the Yes/No test's capacity to effectively navigate the intricacies of language acquisition, proposing a more in-depth investigation into whether this binary and straightforward approach could indeed be beneficial or whether it underscores the imperative for a more holistic and contextually aware method in language instruction by expanding the sample size and including more proficiency levels. Questions remain regarding the efficacy of the Yes/No test. Is it optimally utilized as a straightforward, standalone tool, or should it be considered within the broader spectrum of comprehensive, context-sensitive learning strategies? This inquiry invites a nuanced evaluation of the test's role in language acquisition. It challenges researchers and practitioners to contemplate whether its simplicity is a virtue in isolation or if its true value emerges when integrated with a holistic approach to learning.

How to auscultate for heart sounds in infants and children.

This article provides an introduction to performing cardiac auscultation in infants and children aged 0-16 years, with the aim of improving children's nurses' knowledge and confidence in this procedure. Nurses wishing to develop competence in cardiac auscultation can use this article as a guide to complement supervised practical experience; however, it is not intended to determine an individual's competence. • Cardiac auscultation is a component of a structured cardiac assessment rather than a standalone tool. • It is important to auscultate all four main heart valve areas, listening for normal heart sounds and any additional sounds. • Several differences should be considered when auscultating heart sounds in infants and children compared with adults, including heart rate ranges and the potential need to use distraction techniques for those who are distressed. REFLECTIVE ACTIVITY: 'How to' articles can help to update your practice and ensure it remains evidence based. Apply this article to your practice. Reflect on and write a short account of: • How you think this article might help improve your practice when undertaking cardiac auscultation in infants and children. • How you could use this information to develop your own knowledge and skills before educating others on the appropriate technique and evidence base behind auscultating heart sounds in infants and children.

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/).

DiaTracer enables spectrum-centric analysis of diaPASEF proteomics data.

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

ModelHamiltonian: A Python-scriptable library for generating 0-, 1-, and 2-electron integrals.

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.

How accurate is the QbTest for measuring symptoms of ADHD in children and adults?

This literature review aimed to assess the reliability and validity of the quantified behavioural test (QbTest) to measure the symptoms of attention deficit hyperactivity disorder (ADHD), determining the extent to which results of the selected studies were accurate and generalisable. A literature review was undertaken, with each paper appraised using the Critical Appraisal Skills Programme randomised controlled trial checklist. Thematic analysis was also implemented to identify key themes and relationships between data sets. Three papers concluded that the QbTest increases clinical efficiency without compromising diagnostic accuracy, while others identified limitations relating to its ability to identify symptoms of impulsivity correctly. Additional issues were identified relating to external validity, generalisability and the extent to which the QbTest could differentiate ADHD from other conditions. It is concluded that the QbTest is an unreliable means of assessing ADHD in both children and adults, particularly when used as a stand-alone assessment tool. Thematic analysis highlighted concerns around diagnostic accuracy and the QbTest's inability to differentiate symptoms of ADHD from other conditions – fundamental flaws affecting the overarching fidelity of QbTesting.

Real-world evaluation of Brainomix e-Stroke software

Background and purposeBrainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to...

Leveraging Digital Technologies for Carbon Footprint Tracking in Perennial Cultivations: A Case Study of Walnut Orchard Establishment in Central Greece

The present paper aims to quantify the carbon emissions associated with the establishment of 15 walnut orchards (“Juglans californica”) in the greater area of Magnisia, Greece, with the use of a carbon footprint tool interconnected to a Farm Management Information System. The data collection spanned the first five years following the planting of the trees, providing a comprehensive view of the emissions during this critical establishment phase. Over the five-year period examined (February 2019–December 2023), the results revealed net carbon emissions amounting to 13.71 tn CO2 eq ha−1, with the calculated emissions showing an increasing trend from the first year through the fifth year. Scope 1 (7.38 tn CO2 eq ha−1) and Scope 2 (3.71 tn CO2 eq ha−1) emissions emerged as the most significant, while irrigation (drip irrigation) and fertilizing practices were identified as the highest contributors to emissions. This study highlights the significance of using integrated digital tools for monitoring the performance of cultivations rather than standalone tools that are currently widely available. Integrated tools that incorporate various applications simplify data collection, encourage accurate record-keeping, and facilitate certification processes. By automating data entry and calculations, these tools reduce human error during agricultural carbon management and save time; thus, the integration of digital monitoring tools is vital in improving data accuracy, streamlining certification processes, and promoting eco-friendly practices, crucial for the evolving carbon market.

Developing an Individualized Patient Decision Aid for Chronic Coronary Disease Based on the ISCHEMIA Trial: A Mixed-Methods Study

BACKGROUND: Pursuing initial invasive or conservative management of chronic coronary disease (CCD) is a preference-sensitive decision that should include shared decision-making. Communicating the benefits of either approach is challenging, as individual patients rarely achieve the population-averaged outcomes reported in clinical trials. Our objective was to develop a patient decision aid (PDA) with patient-specific estimates of outcomes for initial invasive versus conservative management of CCD, based on the ISCHEMIA trial (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches). METHODS: This was a multiphase mixed-methods study using focus groups of outpatients with CCD, caregivers, clinicians, and researchers. Focus groups were held in Kansas City, MO and New York City, NY between September 2021 and June 2022. Patients with CCD were included if they had a positive stress test within 1 year. Phase 1 focused on patient priorities for outcomes to guide treatment decisions. Phase 2 involved PDA development and refinement. Phase 3 involved further refinement and member checking. Key themes involving shared decision-making and treatment preferences were elicited from focus groups using a deductive approach to develop a PDA representing the outcomes most important to patients. RESULTS: Of 46 patient and caregiver participants, the mean age was 63.5 years, 53% were female, 61% were White, 24% were Black, and 9% were Hispanic. When deciding between treatments, participants valued shared decision-making but generally deferred decisions to clinicians. The outcomes most important to participants were survival and quality of life, followed by physical functioning and symptoms. To represent these outcomes, participants favored simple visualizations, such as a speedometer or health meter. When deciding between treatment options, participants preferred to use the PDA collaboratively with a clinician instead of as a stand-alone tool. CONCLUSIONS: Our novel, patient-centered approach to developing a PDA for CCD with patient-specific outcomes has the potential to rapidly translate clinical trial results to individual patients and support shared decision-making.

Influenza sequence validation and annotation using VADR

Abstract Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLu ANnotation tool (FLAN) has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions and has been publicly available as a webserver but not as a standalone tool. Viral Annotation DefineR (VADR) is a general sequence validation and annotation software package used by GenBank for norovirus, dengue virus and SARS-CoV-2 virus sequence processing that is available as a standalone tool. We have created VADR influenza models based on the FLAN reference sequences and adapted VADR to accurately annotate influenza sequences. VADR and FLAN show consistent results on the vast majority of influenza sequences, and when they disagree, VADR is usually correct. VADR can also accurately process influenza D sequences as well as influenza A H17, H18, H19, N10 and N11 subtype sequences, which FLAN cannot. VADR 1.6.3 and the associated influenza models are now freely available for users to download and use. Database URL: https://bitbucket.org/nawrockie/vadr-models-flu.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Comparison of the GAD-7 and ImPACT symptom cluster scores in measuring anxiety among college athletes

Assessing mental health needs among college athletes is important given the frequency of symptoms of depression and anxiety and associated complications that may arise after a sports-related concussion. The purpose of this study was to explore the utility of the General Anxiety Disorder-7 (GAD-7) as an independent measure of anxiety symptoms among Division II collegiate athletes (n = 568) during baseline concussion assessments, which also included the post-concussion symptom scale (PCSS) and a measure of depression, the Patient Health Questionnaire-9 (PHQ-9). While only 31 athletes (5.5%) fell above the established GAD-7 cutoff score of 5 or more, a sizable proportion of them (13 or 41.9%) endorsed 0 items on the PCSS affective symptom cluster. Additionally, ∼2% of athletes reported elevated symptoms of anxiety but not depression. These findings support the incorporation of a brief stand-alone anxiety screening tool during concussion baseline evaluations to ensure better detection of student athletes with elevated anxiety symptoms who might otherwise be overlooked, so they may be referred for further diagnostic assessment.

Rethinking Thin-Layer Chromatography for Screening Technetium-99m Radiolabeled Polymer Nanoparticles.

Thin-layer chromatography (TLC) is commonly employed to screen technetium-99m labeled polymer nanoparticle batches for unreduced pertechnetate and radio-colloidal impurities. Although this method is widely accepted, our findings applying radiolabeled PLGA/PLA-PEG nanoparticles underscore its lack of transferability between different settings and its limitations as a standalone quality control tool. While TLC profiles may appear similar for purified and radiocolloid containing nanoparticle formulations, their in vivo behavior can vary significantly, as demonstrated by discrepancies between TLC results and single-photon emission computed tomography (SPECT) and biodistribution data. This highlights the urgent need for a case-by-case evaluation of TLC methods for each specific nanoparticle type. Our study revealed that polymeric nanoparticles cannot be considered analytically uniform entities in the context of TLC analysis, emphasizing the complex interplay between nanoparticle composition, radiolabeling conditions, and subsequent biological behavior.