Sensitive Analysis Research Articles

Emotionally Sensitive Thematic Analysis

ABSTRACT While some qualitative research provides sophisticated accounts of emotions, thematic analysis (TA) – a widely adopted and flexible method in qualitative psychology – could better recognize and account for participant emotions. TA’s versatility presents an opportunity to incorporate more direct attention to these emotions, aligning with consideration of researcher emotions. This opportunity varies across TA’s different forms. Reflexive TA acknowledges participant emotions more effectively than conventional TA by encouraging researchers to engage with their own emotional responses, enhancing their ability to manage participant emotions. Improving emotion recognition is thus more pressing for conventional TA. As the more established form whose guidelines emphasize cognition and behaviour over emotions, conventional TA also provides a clearer baseline for critique and improvement. Therefore, this paper focuses on conventional TA while acknowledging the scope for both forms of TA to code participant emotions more explicitly. It introduces Emotion–Sensitive Thematic Analysis (ESTA), a structured framework that expands ordinary data coding to integrate emotional codes deliberately. Systematically integrating participant emotions into TA sensitizes conventional coding, potentially enhancing its integrity and theme development to broaden and deepen understanding of psychological life’s emotional dimensions. ESTA’s conceptualization is described and its application illustrated for field testing after peer validation.

Urease-Driven Janus Nanomotors for Dynamic Enrichment and Multiplexed Detection of Bladder Cancer MicroRNAs in Urine.

Bladder cancer diagnosis typically involves approaches such as cystoscopy, biopsy, urine cytology, and medical imaging. However, these invasive procedures carry a risk of complications, and direct in vitro detection on clinical samples often results in low sensitivity. Therefore, this study proposed urease-driven magnetic nanomotors for the simultaneous detection of bladder cancer biomarkers miRNA-21 and miRNA-182 in urine samples, aiming for noninvasive diagnosis. The nanomotor was constructed from gold nanorods, mesoporous organo-silica, Fe3O4, and hairpin DNA (hDNA), functioning as a recognition probe for the target miRNAs. In the urea solution, urease catalyzed urea into ammonia and carbon dioxide, propelling the nanomotor for about 60 min, which enhanced the capacity of the probes to capture the target miRNAs. Subsequently, magnetic enrichment enabled highly sensitive dual-miRNA analysis, allowing quantification of miRNA-21 and miRNA-182 with detection limits of 29 and 362 fM, respectively. The nanoprobes also effectively detected miRNAs in spiked urine samples. This simultaneous detection of multiple miRNAs increased the reliability of cancer diagnosis, presenting a novel noninvasive strategy for bladder cancer detection through precise in vitro analysis of actual urine samples.

Carbon Footprint of By-Product Concentrate Feed: A Case Study

Using by-products in livestock feed can be an additional strategy for safeguarding land use in agriculture and reducing the environmental impact of animal production. Studies conducted on farms to assess the environmental impact of milk and meat production using life-cycle assessment (LCA) tools reveal that feeding accounts for approximately one-third. This study aimed to calculate the carbon footprint (CF) of three different concentrated feeds for livestock, both with and without the inclusion of by-products in the formulation. Three different formulations of concentrated feeds for dairy cows were developed homogeneously regarding energy content and crude protein. The LCA approach assessed CF in kg CO2 eq.; the functional unit was 1 kg of concentrate feed. A sensitive analysis of soybean meal’s association with deforestation was formulated. The concentrated feed with by-products demonstrated a lower impact on CF of 23.7% and 37.0% compared to concentrated feed with a mix of raw material and by-products, and solely with raw material, respectively. Using agricultural by-products to produce concentrated feed for livestock sectors can be an environmentally sound alternative in terms of carbon footprint.

Species-specific optimization of oxylipin ionization in LC-MS: a design of experiments approach to improve sensitivity.

Oxylipins are diverse bioactive signaling molecules, which occur in very low concentrations in complex matrices, posing challenges in achieving consistent and sensitive analysis. UHPLC-MS/MS is the preferred technique to separate and quantify these molecules, often optimized using a time-consuming trial-and-error approach. In this study, we applied the design of experiments (DoE) approach to systematically investigate the ionization properties of multiple oxylipin species. Fractional factorial and central composite designs were employed to detect relevant instrument parameters and optimize signal intensity in ESI-MS/MS analysis. Response surface modeling revealed distinct ionization and fragmentation behaviors between polar and apolar oxylipins, driven by their responses to interface temperature and collision-induced dissociation (CID) gas pressure. Particularly, prostaglandins and lipoxins benefit from higher CID gas pressure and lower temperatures compared to the lipophilic HODEs and HETEs to achieve optimal intensity in multiple reaction monitoring analysis. While global source parameters were optimized, analyte-specific entrance/exit potentials and collision energies required individual adjustments. The final method was applied to analyze seven oxylipin classes including leukotrienes, prostaglandins, lipoxins, resolvins, HETEs, HODES, and HoTrEs. Although improvements in lower limits of quantification were modest (< 1pg on-column), signal-to-noise ratios increased two-fold for lipoxins and resolvins and three- to four-fold for leukotrienes and HETEs, enhancing detection at trace levels. This DoE-guided strategy provides a powerful tool to improve UHPLC-MS/MS analysis of oxylipins across various instrument vendors, guiding the way towards inter-laboratory comparability.

Enhanced nano-LC-MS for analyzing dansylated oral cancer tissue metabolome dissolved in solvents with high elution strength.

Tissue metabolomics analysis, alongside genomics and proteomics, offers crucial insights into the regulatory mechanisms of tumorigenesis. To enhance metabolite detection sensitivity, chemical isotope labeling (CIL) techniques, such as dansylation, have been developed to improve metabolite separation and ionization in mass spectrometry (MS). However, the dissolution of hydrophobic derivatized metabolites in solvents with high acetonitrile content limits the use of liquid chromatography (LC) systems with small-volume reversed-phase (RP) columns. In this study, we established a nano-LC-MS system with an online dilution design to address this issue, enabling sensitive analysis of oral cancer tissue metabolomes. Our nano-LC system features a flow path design with online sample dilution before an RP trap column and backflushing of the trap column before entering the analytical column. Compared to other nano-LC systems, both with and without online dilution designs, our system demonstrates the superiority of the T-connector-based dilution method. Using only 1/20th of the sample required for popular micro-LC systems, our nano-LC detects a larger number of peak pairs with similar recovery rates for both hydrophilic and hydrophobic metabolites, ensuring unbiased results. Thirty-two matched pairs of oral squamous cell carcinoma (OSCC) tissue samples and adjacent noncancerous tissues (ANTs) underwent high-throughput CIL-metabolome analysis using our nano-LC system. Compared to our previous micro-LC methods, the nano-LC-MS system exhibits enhanced detection sensitivity, significantly reducing sample requirements. Our findings highlight the efficacy of our platform for metabolomic analysis with limited sample amounts. The nano-LC system's ability to analyze samples dissolved in strong eluents suggests potential applications for handling other hydrophobic compounds using RPLC or other separation methods facing similar solvent incompatibility issues. This approach holds promise for identifying novel metabolite biomarkers for oral cancers, advancing our understanding of tumorigenesis, and enhancing clinical applications.

Lab on a single microbead: An enzyme-free strategy for the sensitive detection of microRNA via efficient localized catalytic hairpin assembly.

Accurate quantification of microRNA (miRNA) is of great significance because it provides opportunities for the accurate early diagnosis of a series of human diseases including cancers. Currently, complicated nucleic acid amplification technologies are always required for the highly sensitive miRNA detection. The introduction of nucleic acid signal amplification coupled with various enzymes will inevitably lead to tedious work and increase the complexity of the analysis process. It is still urgently desired to develop enzyme-free yet sensitive assays that enable the sensitive analysis of miRNA in complicated biological samples. A single microbead (MB)-based localized catalytic hairpin assembly (CHA) strategy is proposed for the sensitive analysis of microRNA (miRNA). This rationally designed CHA strategy allows target miRNA to walk only on a single MB which can create a micro-amplification zone, initiating a highly efficient localized CHA reaction, generating a large number of fluorescent DNA duplexes on the surface of single MB. The efficient localized CHA on single MB can not only greatly suppress the nonspecific reaction between two hairpin probes, thus decreasing the background signal, but also greatly enhance the brightness of MB owing to the highly-concentrated fluorescence enrichment on only one MB. Therefore, highly sensitive quantification of miRNA has been achieved by measuring the fluorescence signal on MB using a confocal fluorescence microscope. This new strategy exhibits a detection limit of 1.09 pM for let-7a detection, and enables high specificity of distinguishing homologous miRNA family members. This is the first report by only using one single MB as a carrier to conduct localized CHA, rendering highly-concentrated fluorescence enrichment on only one MB and a dramatic increase in sensitivity. This single MB-based localized CHA strategy has been successfully applied to the accurate analysis of miRNA target in complex biological sample.

Controllable Regulation of Emissive Excimers with Alkyl Chains for Highly Sensitive Perchlorate Analysis

Controllable Regulation of Emissive Excimers with Alkyl Chains for Highly Sensitive Perchlorate Analysis

Dual Ionic Signal Detection: Modulation of Surface Charge of Nanofluidic Iontronics by Dual-Split Gate Voltages.

Nanofluidic iontronics, including the field-effect ionic diode (FE-ID) and field-effect ionic transistor (FE-IT), represent emerging nanofluidic logic devices that have been employed in sensitive analyses. Making analyte recognitions in predefined nanofluidic devices has been verified to improve the sensitivity and selectivity using a single ionic signal, such as ionic current amplification, rectification, and Coulomb blockade. However, the detection of analytes in complex systems generally necessitates more diverse signals beyond just ionic currents. Here, we demonstrated that dual ionic signals, steady ionic switching ratio, and transient response time (ts) act as detection signals modulated by dual-split gate voltages along the nanochannel for the detection of charged analytes. With an increase in gate voltage, the switching ratio decreases in both FE-ID and FE-IT, whereas the response time exhibits an exponential increase specifically in the FE-ID. Moreover, the response time shows no significant correlation with the external transmembrane voltage in the FE-IT. These results contribute to the optimization of reconfigurable iontronics through gate voltage modulation, providing a theoretical foundation for multiple ionic signal detection.

Developing metalinguistic understanding in the secondary English classroom: the role of teachers’ framing questions and responsive talk moves in dialogic metalinguistic talk about written text

ABSTRACT In this paper, whole class episodes of metalinguistic talk are examined for their potential to support the development of Key Stage Three (KS3, age 11–14) learners’ metalinguistic understanding of how linguistic choice shapes meaning in written text. Drawing on data from the first phase of a three-year ESRC funded project, this paper explores how teachers’ framing questions foreground and manoeuvre learners’ attention to written text, opening differently framed lines of enquiry that are sustained and advanced by teachers’ responsive talk moves. This paper proposes that teachers’ skilful orchestration of framing questions and responsive talk moves operate to develop metalinguistic understanding by connecting personal, tacit, or partially formed understandings with more explicit verbalisation of the relationship between linguistic choice and rhetorical effect. Presenting a contextually and disciplinary sensitive analysis of dialogic talk in the writing classroom, this paper is of empirical and theoretical significance to the field of dialogic talk, educational linguistics, and writing research, and advances the theorisation of pedagogical approaches that may empower learners to convey and communicate their own meanings and intentions in writing.

Simultaneous Confidence Regions for Image Excursion Sets: a Validation Study with Applications in fMRI.

Functional Magnetic Resonance Imaging (fMRI) is commonly used to localize brain regions activated during a task. Methods have been developed for constructing confidence regions of image excursion sets, allowing inference on brain regions exceeding non-zero activation thresholds. However, these methods have been limited to a single predefined threshold and brain volume data, overlooking more sensitive cortical surface analyses. We present an approach that constructs simultaneous confidence regions (SCRs) which are valid for all possible activation thresholds and are applicable to both volume and surface data. This approach is based on a recent method that constructs SCRs from simultaneous confidence bands (SCBs), obtained by using the bootstrap on 1D and 2D images. To extend this method to fMRI studies, we evaluate the validity of the bootstrap with fMRI data through extensive 2D simulations. Six bootstrap variants, including the nonparametric bootstrap and multiplier bootstrap are compared. The Rademacher multiplier bootstrap-t performs the best, achieving a coverage rate close to the nominal level with sample sizes as low as 20. We further validate our approach using realistic noise simulations obtained by resampling resting-state 3D fMRI data, a technique that has become the gold standard in the field. Moreover, our implementation handles data of any dimension and is equipped with interactive visualization tools designed for fMRI analysis. We apply our approach to task fMRI volume data and surface data from the Human Connectome Project, showcasing the method's utility.

High-Sensitivity Fluorescent Sensing Platform for Rapid and Selective Detection of Chlorpyrifos Pesticide in Water Samples Using Terbium-Isatin-3-allyl Complex.

The detection of organophosphorus pesticides, particularly chlorpyrifos, in environmental samples is essential due to their widespread use and associated health risks. In this study, we developed a high-sensitivity fluorescent sensing platform utilizing an Isatin-3-allyl-terbium (IS-Tb) complex in solution for the rapid and selective detection of chlorpyrifos in various water samples. The proposed chemical structure of the complex in solution was evaluated using molar ratio method. The IS-Tb complex was characterized in solution, as it was not isolated in the solid state. To assess its structural and optical properties, several characterization techniques were employed. UV-Vis spectrophotometry was used to evaluate the absorption characteristics, and spectrofluorimetry was applied to assess the fluorescence properties. Additionally, the stoichiometry of the complex was determined using the molar ratio method, which confirmed a 1:1 ligand-to-metal ratio and provided the optimal conditions for sensing chlorpyrifos. The IS-Tb complex exhibited weak intrinsic fluorescence, which was significantly enhanced upon interaction with chlorpyrifos, enabling sensitive and quantitative analysis. Methanol was identified as the best solvent for maximizing the sensitivity of chlorpyrifos detection. The sensor demonstrated a wide linear detection range from 0.006 to 28µg/L, with a low limit of detection (LOD) of 2 ng/L. The sensor's selectivity for chlorpyrifos over other commonly encountered pesticides (13 organophosphorus) and environmental contaminants was high, ensuring minimal interference. The performance of the sensor was validated using spiked water samples, yielding an average recovery of 101% with a relative standard deviation (RSD) of less than 5%. These results suggest that the proposed sensing platform is a powerful tool for the environmental monitoring of chlorpyrifos, with significant implications for public health protection and regulatory compliance.

The Relationship Between Body Mass Index and Peripheral Artery Disease: Insights from theNational Health andNutritionExamination Survey and Mendelian Randomization Analysis.

Body mass index (BMI) has been implicated in various cardiovascular conditions, but its association with peripheral artery disease (PAD) in both real-world and genetic studies have been contentious and debated. This study enrolled 6707 individuals from the National Health andNutritionExamination Survey database to investigate the association between BMI and the risk of PAD. The weighted logistic regression, restricted cubic spline, and subgroup analysis were performed using real-world data. Mendelian randomization study was conducted using genetic data from the Genome-Wide Association Study. The inverse variance weighted method was used as the primary analysis approach, and a sensitivity analysis was conducted to identify pleiotropy and heterogeneity bias. Individuals with PAD had higher mean BMI values compared to those without PAD (28.82±5.87 and 28.31±5.42, P=0.007). For the categorical variable of BMI, individuals in obesity class 2 (odds ratio [OR]=1.532, 95% CI=1.082-2.169; P=0.013) and obesity class 3 (OR=2.479, 95% CI=1.515-4.056; P<0.001) had a higher risk of PAD analyzed by weighted logistic regression. Subgroup analysis revealed that the association between BMI and PAD persisted. Given that a higher BMI is associated with PAD, we selected obesity for Mendelian randomization analysis and observed that obesity had a relationship with PAD (inverse variance weight: OR=1.194, 95% CI=1.099-1.296; P<0.001). The reliable findings were validated by sensitive analysis (all P>0.05). BMI is a robust risk factor for PAD. A higher BMI (especially ≥35kg/m2) is associated with an increased risk of developing PAD. Meanwhile, there is a causal relationship between obesity and PAD. Interventions are necessary for targeted obesity prevention and management strategies for PAD.

Nanoparticle-assisted PCR: fundamentals, mechanisms, and forensic implications.

Polymerase Chain Reaction (PCR) has transformed forensic DNA analysis butis still limited when dealing with compromised trace or inhibitor-containing samples. Nanotechnology has been integrated into nanoPCR (nanoparticle-assisted PCR) to overcome these obstacles. Nanomaterials improve PCR sensitivity, selectivity, and efficiency. Examples of these materials are semiconductor quantum dots and metal nanoparticles. They enhance DNA binding to primers, stabilize enzymes, and function as effective heat conductors, making accurate amplification possible even with tainted samples. The developments in nanoPCR have potential uses in forensics, as they allow for the more sensitive analysis of smaller, polluted, or deteriorated samples. Nevertheless, there are methodological and ethical issues. To provide credible and legitimate forensic evidence, rigorous validation and standardization of NanoPCR techniques are vital. The article addresses the relevant ethical and methodological aspects in forensic casework while examining the integration of nanotechnology into PCR.

High-Efficiency Electrochemiluminescence Biosensor with Antifouling and Antibacterial Functions for Sensitive and Accurate Analysis of Chloramphenicol in Seawater.

In marine environmental monitoring, due to the presence of a large number of interfering proteins and bacteria in seawater, it is of great significance to construct an efficient sensing interface with antifouling and antibacterial functions to avoid the aforementioned interferences. On this basis, the zwitterionic hydrogel based on sulfobetaine methacrylate (SBMA) and bovine serum albumin (BSA) was developed as an antifouling and antibacterial coating. The combination of hydration of zwitterions and hydrophilicity of hydrogels endows BSA@PSBMA with good antiadsorption ability, which effectively hinders the adhesion of proteins and bacteria, thereby improving the detection sensitivity of the biosensor. At the same time, the covalent grafting of SBMA and BSA solves the defect of poor stability of individual BSA in complex matrices, improving the stability and service life of the biosensor. In addition, Au@luminol was encapsulated in the zwitterionic hydrogel as an internal standard to realize a one-step integration of antifouling and ratio strategies, which simplifies the construction process of the biosensor. The developed electrochemiluminescence biosensor exhibited good sensitivity and accuracy for chloramphenicol detection, with a wide linear range of 1 pM to 100 nM and a low detection limit of 0.39 pM (S/N = 3), which is suitable for trace detection of antibiotics in seawater.

Compositional data analysis enables statistical rigor in comparative glycomics

Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious “decreases” of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.

Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)

This review explores the advances in developing adsorbent materials for solid-phase microextraction (SPME), focusing on nanoparticles, nanocomposites, and nanoporous structures. Nanoparticles, including those of metals (e.g., gold, silver), metal oxides (e.g., TiO2, ZnO), and carbon-based materials (e.g., carbon nanotubes, graphene), offer enhanced surface area, improved extraction efficiency, and increased selectivity compared to traditional coatings. Nanocomposites, such as those combining metal oxides with polymers or carbon-based materials, exhibit synergistic properties, further improving extraction performance. Nanoporous materials, including metal–organic frameworks (MOFs) and ordered mesoporous carbons, provide high surface area and tunable pore structures, enabling selective adsorption of analytes. These advanced materials have been successfully applied to various analytes, including volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), pesticides, and heavy metals, demonstrating improved sensitivity, selectivity, and reproducibility compared to conventional SPME fibers. The incorporation of nanomaterials has significantly expanded the scope and applicability of SPME, enabling the analysis of trace-level analytes in complex matrices. This review highlights the significant potential of nanomaterials in revolutionizing SPME technology, offering new possibilities for sensitive and selective analysis in environmental monitoring, food safety, and other critical applications.

Enhanced Electrochemiluminescence from Ruthenium-Tagged Immune Complex at Flexible Chains for Sensitive Analysis of Glutamate Decarboxylase Antibody.

Herein, a sensitive electrochemiluminescence (ECL) immunosensor is designed by immobilizing ruthenium-tagged immune complexes at flexible poly-ethylene-glycol (PEG) chains on the electrode surface, which offers more freedom for the collision of the ruthenium complex at the electrode during the initial ECL reaction. The electrochemical characterizations confirm the loose structure of the assembled layer with the immune complex, providing an increase in the current and the resultant enhanced ECL emissions. Comparing the sensors with the rigid structure, a 34-fold increase in the maximal ECL emission is recorded when PEG3400 is used as a linker. Using the optimized protocol, the prepared immunosensor exhibits a wide-ranging linear response to the model antibody (glutamate decarboxylase antibody) ranging from 10 pg/mL to 10 ng/mL. The detection limit is almost two orders lower than the value using the classic enzyme-linked immunosorbent assay, which offers a new design to enhance ECL emissions and the resultant analytical performance.

Directed Evolution of Multicyclic Peptides Using Yeast Display for Sensitive and Selective Fluorescent Analysis of CD28 on the Cell Surface.

CD28 is a costimulatory receptor that provides the second signal necessary for T-cell activation and is associated with diseases, including rheumatoid arthritis, asthma, and cancer. Targeting CD28 is crucial for both functional bioanalysis and therapeutic development. Molecular probes, particularly fluorescent probes, can enhance our understanding of CD28's cellular roles. However, existing antibody-based probes face challenges such as high production costs, low stability, and large size, which limit their bioanalytical applications. Thus, there is a need for smaller, robust probes that enable the sensitive and selective targeting of CD28. Multicyclic peptides have emerged as promising candidates for novel therapeutics and molecular probes. Recently, we identified disulfide-directed multicyclic peptides (DDMPs) that bind CD28 with submicromolar affinity; however, their relatively low affinity limits further applications. In this study, we develop a DDMP evolving system based on yeast display and error-prone PCR to identify high-affinity peptide binders. We obtained DDMPs with a picomolar affinity for CD28, exceptional binding specificity, and remarkable oxidative folding efficiency. Furthermore, we developed fluorescent probes and labeling strategies for detecting and visualizing CD28 expression in human T cells. This advancement opens new avenues for studying T-cell dynamics and activation states, which are essential for understanding immune responses and developing targeted therapies. Our study not only produces potent CD28 binders and probes but also establishes a robust platform for optimizing other multicyclic peptide-based probes and therapeutics.

Anomaly detection of gear wear: introducing a novel health indicator through experimentation and physical investigation

Monitoring gear wear is important for gear diagnosis, although it remains a challenging endeavor. Unlike localized tooth faults, there are still physical patterns in the vibration signature associated with distributed wear faults that should be unfolded. This study contributes a novel framework for gear wear diagnosis, supported by physical principles and validated against traditional methods through extensive experimentation. First, we characterize gear wear in the vibration signature, utilizing data from dozens of seeded, realistic degrading wear cases across various rotational speeds collected through unique controlled-degradation tests. We examine conventional features that have proven useful for diagnosing localized tooth faults and demonstrate their inadequate performance in diagnosing gear wear. We propose a sensitive spectral analysis of the gear mesh and sideband spectral energy in the spectrum to recognize wear manifestation and monitor its degradation. Drawing on the insights gained in this study, we introduce a novel health indicator for anomaly detection of incipient gear wear, grounded in physical understanding and employing sophisticated yet simple feature engineering techniques. We analyze the performance of the proposed health indicator, demonstrating its superiority over state-of-the-art methods, and underscore the importance of considering assembly and dismantling operations to avoid the issue of test-training leakage. Additionally, we highlight the potential of the proposed physical-based framework combined with a deep-learning approach to detect wear in its early stages. To the best of our knowledge, this is the first study to conduct extensive controlled-degradation tests, rather than endurance tests, to investigate gear wear and offer a hybrid physical data-driven framework that combines advanced signal processing, feature extraction, and feature engineering techniques for early anomaly detection.

FastSurfer-LIT: Lesion Inpainting Tool for Whole Brain MRI Segmentation With Tumors, Cavities and Abnormalities

Abstract Resection cavities, tumors, and other lesions can fundamentally alter brain structure and present as abnormalities in brain MRI. Specifically, quantifying subtle neuroanatomical changes in other, not directly affected regions of the brain is essential to assess the impact of tumors, surgery, chemo/radiotherapy, or drugtreatments. However, only a limited number of solutions addresses this important task while many standard analysis pipelines simply do not support abnormal brain images at all. In this paper, we present a method to perform sensitive neuroanatomical analysis of healthy brain regions in the presence of large lesions and cavities. Our approach called “FastSurfer Lesion Inpainting Tool” (FastSurfer-LIT) leverages the recently emerged Denoising Diffusion Probabilistic Models (DDPM) to fill lesion areas with healthy tissue that matches and extends the surrounding tissue. This enables subsequent processing with established MRI analysis methods such as the calculation of adjusted volume and surface measurements using FastSurfer or FreeSurfer. FastSurfer-LIT significantly outperforms previously proposed solutions on a large dataset of simulated brain tumors (N=100) and synthetic multiple sclerosis lesions (N=39) with improved Dice and Hausdorff measures, and also on a highly heterogeneous dataset with lesions and cavities in a manual assessment (N=100). Finally, we demonstrate increased reliability to reproduce pre-operative cortical thickness estimates from corresponding post-operative temporo-mesial resection surgery MRIs. The method is publicly available at https://github.com/Deep-MI/LIT and will be integrated into the FastSurfer toolbox.