Reliable Quantification Research Articles

MicronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI , a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI , single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R 2 of 0.87 and a Pearson's correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R 2 of 0.90, and a Pearson's correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Myotonometry and extended field-of-view ultrasound imaging allow reliable quantification of patellar tendon stiffness and length at rest and during maximal load, whereas several restrictions exist for the Achilles tendon.

Stiffness and length are well-established tendon parameters in sports and medicine. Myotonometry and ultrasound imaging are the commonly used methods to quantify these parameters. However, further studies are needed to clarify the reliability of these methods, especially when assessing maximally loaded tendons and when conducted by different experienced investigators. This study aimed to determine the intra- and interrater reliabilities of measuring the stiffness and length of the patellar tendon (PT) and Achilles tendon (AT) using the myotonometry method and the extended field-of-view ultrasound (EFOV-US) technique at rest and maximal load performed by different experienced investigators. Twenty-seven participants were examined on three different days by one experienced investigator and one novice investigator. Primary outcomes were the intraclass correlation coefficient (ICC) and associated 95% confidence interval (95% CI), coefficient of variation (CV), standard error of measurement (SEM), and minimal detectable change (MDC) across the measurement days and investigators. For PT measurements at rest and maximal load, the estimated ICCs for stiffness and length were ≥.867 and ≥.970, respectively, with 95% CIs ranging from poor (.306) to excellent (.973) and good (.897) to excellent (.999). The CV, SEM, and MDC for PT stiffness and length were ≤5.2% and ≤2.0%, ≤39.3 N/m and ≤0.9 mm, and ≤108.9 N/m and ≤2.6 mm, respectively. For AT measurements, some restrictions were evident for stiffness at rest and both parameters at maximal load. However, regarding AT length at rest, the estimated ICC was ≥.996, with an excellent 95% CI (.987-.999). The CV, SEM, and MDC for AT length at rest were 2.8%, ≤1.1 mm, and ≤2.9 mm, respectively. The estimated ICCs show good to excellent reliability for the myotonometry method and the EFOV-US technique for measuring PT stiffness and length at rest and maximal load for experienced and novice investigators. However, some restrictions are evident for the AT, especially for measurements at maximal load.

Parvalbumin Gene: A Valuable Marker for Pike Authentication and Allergen Risk Assessment.

Fish from the pike (Esox) genus are valued in gastronomy for their superior meat quality. However, they can cause allergic reactions in sensitive consumers. This work aimed to fill the gap in the detection of pike allergens using molecular-biological techniques. New, fast, and accurate loop-mediated isothermal amplification (LAMP) and real-time PCR (qPCR) assays were designed to detect pike DNA using the parvalbumin gene as a marker. LAMP was assessed by electrophoresis, SYBR green optical detection, and real-time fluorescence detection. The latter was the most sensitive, detecting as little as 0.78 ng of pike DNA; the qPCR detection limit was 0.1 ng. The LAMP analysis took 20-70 min, which is significantly faster than qPCR. The study provides reliable detection and quantification of the parvalbumin gene in both fresh and processed samples and further highlights the versatility of the use of the parvalbumin gene for the authentication of food products and consumer protection via refined allergen risk assessment that is independent of the type of tissue or food processing method used.

The textures of sarcoidosis: quantifying lung disease through variograms.

Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.

Applying improved ddPCR to reliable quantification of MPXV in clinical settings.

The ddPCR technique proved to be a sensitive and valuable tool for accurately quantifying MPXV viral loads in various clinical specimen types. The findings provided valuable insights into the necessary pre-treatment protocols for MPXV diagnosis in ddPCR detection and the potentially suitable sample types for collection. Therefore, such results can aid in comprehending the potential characteristics of MPXV infection and the usage of ddPCR in clinical settings.

Practical lessons from implementing a top-down, drone-based methane emissions quantification technology into global oil and gas operations

Reliable top-down quantification of methane emissions from oil and gas operations is increasingly crucial but challenging. Many new technologies are emerging that aim to offer solutions, but they vary widely in their actual field performance, cost, and breadth of applicability. Operators need to evaluate and test different technologies, which can be time-consuming and complex. Once a technology has been selected for use, then typically field trials follow before wider introduction to the operation. This process takes considerable time and effort, and frequently requires extensive learning by the teams involved. Additionally, interpretation of performance often depends upon measurement use-case (e.g. regulatory, voluntary, or leak detection and repair). This paper will share some of the learnings made by the users and technology provider in the assessment and global introduction of a high sensitivity drone-based methane emissions detection, localisation, and quantification technology. There will be a particular focus on practical, operational considerations for such introductions to help accelerate the ongoing efforts of others interested in implementing emissions quantification technologies. Examples from Australia and further afield (both onshore and offshore) will be used to illustrate key points within the paper which include the value of third-party technology validation, consistency across multiple operations and scalability/availability of services.

Rapid and reliable quantification of urinary malondialdehyde by HILIC-MS/MS: A derivatization-free breakthrough approach

BackgroundThe development of fast analytical methods is crucial for the research, discovery, and confirmation of crucial biomarkers. Furthermore, the implementation of fast analytical strategies contributes to efficient and time-effective procedures. In this sense, analysis of malondialdehyde (MDA) has become an important tool for understanding the role of oxidative stress in various diseases and for evaluating the efficacy of therapeutic interventions. ResultsA rapid and robust liquid chromatography tandem mass spectrometry method (HPLC-MS/MS) has been developed to determine endogenous amounts of malondialdehyde (MDA) in human urine without any associated derivatization reaction. MDA was separated in 4 min through a Urea-HILIC column and was analyzed using a triple quadrupole mass spectrometer in negative electrospray ionization mode. With a 50-fold dilution as the only sample pretreatment after alkaline hydrolysis, no matrix effect was present, which allowed for a fast and simple quantification by means of an external standard calibration with a limit of detection of 0.20 ng mL−1. The whole methodology was validated by analyzing unspiked and spiked urine samples from ten healthy individuals and comparing with the results obtained by the standard addition method. MDA was detected in all cases, with natural concentrations varying from 0.11 ± 0.03 to 0.31 ± 0.03 mg g−1 creatinine. Accuracies were found to be satisfactory, ranging from 95 % to 101 %. The proposed method also exhibited good repeatability and reproducibility (RSD<15 %) for four quality control levels. SignificanceThe main significance of this method is the avoidance of a derivatization reaction for the determination of urinary MDA, this constituting a step forward when compared with previous literature. This breakthrough not only streamlines time analysis to less than 5 min per sample but also results in a more robust procedure. Consequently, the method here developed could be applied to subsequent future research involving the determination of MDA as a lipid peroxidation biomarker, where simple, rapid, and reliable methods could represent a significant improvement.

Freddy: breaking record for tropical cyclone precipitation?

Abstract Depending on the location on the Earth, the amount of precipitation associated with tropical cyclones (TCs) can reach 20% of the total yearly precipitation over land and up to 40% over some ocean regions. TC induced freshwater flooding has been suggested to be the largest threat to human lives due to TCs. Therefore, a reliable quantification of the precipitation amount associated with each past TC is important for a better definition of the TC fingerprint on the climate. The temporal and horizontal resolution of state-of-the-art observational datasets and atmospheric reanalysis gives the possibility to quantify precipitation associated with TCs globally following the observed TC tracks. In this work we compare the TC-related precipitation in various observational and reanalysis datasets. A particular focus is given to the record-breaking TC Freddy (Southern Indian Ocean, 2023). Here we show that the time-varying bias in TC associated precipitation, due to the positive trend in assimilated observations, makes it difficult to assess long-term trend investigation based on reanalysis. To this aim we need to build on state-of-the-art general circulation models, free to evolve under historical radiative forcing.

Preparation of surface molecularly‐imprinted magnetic kaolin for selective separation and determination of thiabendazole

AbstractIn this study, a selective sorbent was fabricated by combining a surface molecularly‐imprinted polymer and magnetic kaolin for reliable quantification of thiabendazole (TBZ) coupled with spectrofluorimetry. Several surface characterization methods were conducted to confirm the synthesis of TBZ‐imprinted magnetic kaolin with a polymer thickness of ~48 nm. The molecularly‐imprinted magnetic kaolin (MIP@MKLN) showed high adsorption capacity (8.7 mg/g), remarkable selectivity with an imprinting factor of 3.71 and excellent regeneration ability for up to 10 uses without significant change of its initial adsorption capacity for TBZ. The proposed method had linear range from 5 to 500 μg/mL TBZ concentration with a correlation coefficient of 0.995 and the limit of detection was 1.4 μg/mL. MIP@MKLN was used for selective separation and quantification of TBZ in orange and lemon samples. There were high recovery values ranging from 96.4% to 100.2% with lower relative standard deviations (less than 5%) in the spiked samples under optimized conditions. Overall, the proposed method based on MIP@MKLN as an excellent sorbent could produce a feasible, convenient, and promising method for rapid, selective, and sensitive quantification of TBZ in real samples.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Efficiencies of filtration sampling and extraction for recovery of viable Staphylococcus aureus bioaerosols

Airborne Staphylococcus aureus is detected in various locations and linked to human infection. Reliable quantification of viable S. aureus bioaerosols by filter-based samplers helps characterize personal exposure, requiring efficient sampling and post-sampling processing. However, the efficiencies of filtration sampling and cell extraction are undetermined for viable S. aureus. In coupled with quantitative PCR and propidium monoazide, the performance of three widely-used samplers (IOM, Button, and Cassette) loaded with polycarbonate (PC) or gelatin filters was evaluated over 30–270 min of sampling and compared to that of BioSampler containing deionized water (DW). Effects of sampler type, filter type, and sampling time on cell recovery efficiency of sampling methods were assessed. Methods to extract cells from filters were also studied. The 1.5-min vortexing in peptone-Tween mixture and 10-min heating in DW were respectively granted optimal for cell extraction from PC and gelatin filters with extraction efficiencies averaged 1.0–1.76 (n = 4). Both Button and IOM with 3-μm gelatin filter performed best to capture S. aureus, significantly greater than Button with 0.8-μm PC by a factor of 9–11 (P < 0.05) and Cassette or IOM with 0.2-μm PC by a factor of 15–79 (P < 0.05). Cassette and IOM with 0.2-μm PC also showed less efficiencies than BioSampler/DW by a factor of 4–16 (P < 0.05). Cell recovery efficiency was not affected by sampling time except for the Button with 0.8-μm PC. Overall, filter type is the most critical factor governing cell recovery efficiency. Button and IOM with gelatin filter and 10-min heating in DW are considered the most efficient filtration sampling and extraction methods for viable S. aureus.

Enzymatic detection of α-hydroxybutyrate, an important marker of insulin resistance, and comparison with LC-MS/MS detection

AimThe metabolite α-hydroxybutyrate (α-HB) is an important marker of insulin resistance and impaired glucose tolerance allowing to identify patients at risk of developing diabetes and related metabolic disorders before any symptoms become apparent. At present, its exact quantification requires mass spectrometry (LC-MS), which is not compatible with routine laboratory use. Accordingly, a simple enzymatic-based method was assessed and its applicability and measuring accuracy compared with LC-MS was investigated. MethodsStandards, serum, and plasma samples containing α-HB were prepared with routine procedures and their α-HB contents measured with the XpressGT® enzymatic test kit photometrically or with LC-MS and multiple reaction monitoring. Resultsα-HB detection with XpressGT® yielded highly linear calibration curves and 102 % recovery of stocks added to commercial samples. Stability of the analyte in serum and plasma samples prepared with various anti-coagulants was >90 % after 46 h for several widely used preparations and recovery after 3 freeze-thaw cycles was ≥95 % with these anti-coagulants. A direct comparison of 75 samples indicated very good agreement of α-HB levels determined by both methods, 86 % of XpressGT® samples being within ±20 % of LC-MS values and even 93 % within ±20 % considering only samples above 30 μM concentration. ConclusionXpressGT®-based detection of α-HB is an easily applicable method which can be used for accurate and reliable quantification of the metabolite in clinical practice. Routine α-HB determination in patients at risk of developing diabetes would allow early establishment of preventive measures or pharmacological intervention reducing the risk for the onset of serious diabetes-related health problems.

Utilizing novel Escherichia coli-specific conserved signature proteins for enhanced monitoring of recreational water quality.

Escherichia coli serves as a proxy indicator of fecal contamination in aquatic ecosystems. However, its identification using traditional culturing methods can take up to 24h. The application of DNA markers, such as conserved signature proteins (CSPs) genes (unique to all species/strains of a specific taxon), can form the foundation for novel polymerase chain reaction (PCR) tests that unambiguously identify and detect targeted bacterial taxa of interest. This paper reports the identification of three new highly-conserved CSPs (genes), namely YahL, YdjO, and YjfZ, which are exclusive to E. coli/Shigella. Using PCR primers based on highly conserved regions within these CSPs, we have developedquantitative PCR (qPCR) assays for the evaluation of E. coli/Shigella species in water ecosystems. Both in-silico and experimental PCR testing confirmed the absence of sequence match when tested against other bacteria, thereby confirming 100% specificity of the tested CSPs for E. coli/Shigella. The qPCR assays for each of the three CSPs provided reliable quantification for all tested enterohaemorrhagic and environmental E. coli strains, a requirement for water testing. For recreational water samples, CSP-based quantification showed a high correlation (r > 7, p < 0.01) with conventional viable E. coli enumeration. This indicates that novel CSP-based qPCR assays for E. coli can serve as robust tools for monitoring water ecosystems and other critical areas, including food monitoring.

“Small is beautiful” – Examining reliable determination of low-abundant therapeutic antibody glycovariants

Glycans associated with biopharmaceutical drugs play crucial roles in drug safety and efficacy, and therefore, their reliable detection and quantification is essential. Our study introduces a multi-level quantification approach for glycosylation analysis in monoclonal antibodies (mAbs), focusing on minor abundant glycovariants. Mass spectrometric data is evaluated mainly employing open-source software tools. Released N-glycan and glycopeptide data form the basis for integrating information across different structural levels up to intact glycoproteins. Comprehensive comparison showed that indeed, variations across structural levels were observed especially for minor abundant species. Utilizing modification finder (MoFi), a tool for annotating mass spectra of intact proteins, we quantify isobaric glycosylation variants at the intact protein level. Our workflow's utility is demonstrated on NISTmAb, rituximab and adalimumab, profiling their minor abundant variants for the first time across diverse structural levels. This study enhances understanding and accessibility in glycosylation analysis, spotlighting minor abundant glycovariants in therapeutic antibodies.

Non-negative matrix factorization-aided phase unmixing and trace element quantification of STEM-EDXS data

Energy-dispersive X-ray spectroscopy (EDXS) mapping with a scanning transmission electron microscope (STEM) is commonly used for chemical characterization of materials. However, STEM-EDXS quantification becomes challenging when the phases constituting the sample under investigation share common elements and overlap spatially. In this paper, we present a methodology to identify, segment, and unmix phases with a substantial spectral and spatial overlap in a semi-automated fashion through combining non-negative matrix factorization with a priori knowledge of the sample. We illustrate the methodology using a sample taken from an electron beam-sensitive mineral assemblage representing Earth's deep mantle. With it, we retrieve the true EDX spectra of the constituent phases and their corresponding phase abundance maps. It further enables us to achieve a reliable quantification for trace elements having concentration levels of ∼100 ppm. Our approach can be adapted to aid the analysis of many materials systems that produce STEM-EDXS datasets having phase overlap and/or limited signal-to-noise ratio (SNR) in spatially-integrated spectra.

Exonuclease-iii -propelled DNAzyme cascade for sensitive and reliable cervical cancer related miRNA analysis

MicroRNAs (miRNAs) can serve as biomarkers for early-diagnosis, therapy, and postoperative care of cervical cancer. Sensitive and reliable quantification of miRNA remains a huge challenge due to its low expressing levels and background interference. Herein, we propose a novel exonuclease-III (Exo–III)–propelled DNAzyme cascade for sensitive and high-efficient miRNA analysis. This method involves the engineering of compact DNAzyme hairpin probes, including the H1 probe and H2 probe. The H1 probe is designed with exposed analyte recognition subunits that can specifically recognize target miRNA. This recognition triggers two processes: Exo-iii-assisted target regeneration and successive substrate cleavage catalyzed by DNAzyme. The unique character of Exo-III that catalyzes removal of mononucleotides from the blunt or recessed 3′-OH termini of dsDNA confers the approach with a minimal background signal. The multiple signal cycles provided an abundant signal amplification and consequently, the method exhibited a low limit of detection of 3.12 fM, and a better specificity over several homologous miRNAs. In summary, this powerful Exo-III driven DNAzyme cascaded system offers broader and more adaptable methods for comprehending the activities of miRNA in various biological occurrences.

Differentiation and quantification of extracellular polymeric substances from microalgae and bacteria in the mixed culture

Extracellular polymeric substances (EPS) play significant roles in the formation, function, and interactions of microalgal-bacteria consortia. Understanding the key roles of EPS depends on reliable extraction and quantification methods, but differentiating of EPS from microalgae versus bacteria is challenging. In this work, cation exchange resin (CER) and thermal treatments were applied for total EPS extraction from microalgal-bacteria mixed culture (MBMC), flow cytometry combined with SYTOX Green staining was applied to evaluate cell disruption during EPS extraction, and auto-fluorescence-based cell sorting (AFCS) was used to separate microalgae and bacteria in the MBMC. Thermal extraction achieved much higher EPS yield than CER, but higher temperature and longer time reduced cell activity and disrupted the cells. The highest EPS yield with minimal loss of cell activity and cell disruption was achieved using thermal extraction at 55℃ for 30 min, and this protocol gave good results for MBMC with different microalgae:bacteria (M:B) mass ratios. AFCS combined with thermal treatment achieved the most-efficient biomass differentiation and low EPS loss (<4.5 %) for the entire range of M:B ratios. EPS concentrations in bacteria were larger than in microalgae: 42.8 ± 0.4 mg COD/g TSS versus 9.19 ± 0.38 mg COD/g TSS. These findings document sensitive and accurate methods to extract and quantify EPS from microalgal-bacteria aggregates.

MR Diffusion-Weighted Imaging in Evaluating Immediate HIFU Treatment Response of Uterine Fibroids.

Nowadays, High Intensity Focused Ultrasound (HIFU) is a common surgery option for the treatment of uterine fibroids in China, the immediate response of which is clinically evaluated using Contrast Enhanced (CE) imaging. However, the injection of gadolinium with its potential adverse effect is of concern in CE and therefore, it deserves efforts to find a better imaging method without the need for contrast agent injection for this task. To assess the role of diffusion-weighted imaging (DWI) in evaluating the immediate therapeutic response of HIFU treatment for uterine fibroids in comparison with CE. 68 patients with 74 uterine fibroids receiving HIFU treatment were enrolled, and immediate treatment response was assessed using post-surgical DWI images. Semi-quantitative ordinal ablation quality grading and quantitative nonperfusion volume (NPV) measurement based on DWI and CE imaging were determined by two experienced radiologists. Agreement of ablation quality grading between DWI and CE was assessed using the weighted kappa coefficient, while intraobserver, interobserver and interprotocol agreements of NPV measurements within and between DWI and CE were evaluated using the intraclass correlation (ICC) and Bland-Altman analysis. Grading of immediate HIFU treatment response showed a moderate agreement between DWI and CE (weighted kappa = 0.446, p < 0.001). NPV measured in 65 fibroids with DWI of Grade 3~5 showed very high ICCs for the intraobserver and interobserver agreement within DWI and CE (all ICC > 0.980, p < 0.001) and also for the interprotocol agreement between DWI and CE (ICC = 0.976, p < 0.001). DWI could provide satisfactory ablation quality grading, and reliable NPV quantification results to assess immediate therapeutic responses of HIFU treatment for uterine fibroids in most cases, which suggests that non-contrast enhanced DWI might be potentially used as a more costeffective and convenient method in a large proportion of patients for this task replacing CE imaging.