Deep learning image analysis models pretrained on daily objects are useful for the preliminary characterization of particulate pharmaceutical samples.

Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput instrumentation and analyzing the populations data. The analysis includes conventional metrics such as the particle size distribution but can be more sophisticated by interpreting other visual/morphological features. To avoid the hurdles of building new image analysis models capable of extracting such relevant features from scratch, we propose using well-established pretrained deep learning image analysis models such as EfficientNet. We demonstrate that such models are useful as a prescreening tool for high-level characterization of biopharmaceutical particle image data. We show that although these models are originally trained for completely different tasks (such as the classification of daily objects in the ImageNet database), the visual feature vectors extracted by such models can be useful for studying different types of subvisible particles. This applicability is illustrated through multiple case studies: (i) particle risk assessment in prefilled syringe formulations containing different particle types such as silicone oil, (ii) method comparability with the example of accelerated forced degradation, and (iii) excipient influence on particle morphology with the example of Polysorbate 80 (PS80). As examples of agnostic applicability of pretrained models, we also elucidate the application to two high-throughput microscopy methods: microflow and background membrane imaging. We show that different particle populations with different morphological and visual features can be identified in different samples by leveraging out-of-the-box pretrained models to analyze images from each sample.

The Use of Superb Microvascular Imaging in Evaluating Rheumatic Diseases: A Systematic Review.

Background and Objectives: Superb microvascular imaging is an advanced Doppler algorithm that seems to be useful in detecting low-velocity blood flow without using a contrast agent. Increasing evidence suggests that SMI is a more sensitive tool than conventional Doppler techniques for evaluating rheumatic diseases, especially inflammatory arthritis. We aimed to assess the use of SMI in evaluating joints and extraarticular structures. Materials and Methods: Two reviewers independently reviewed the literature to provide a global overview of the possibilities of SMI in rheumatology. Original English-language articles published between February 2014 and November 2022 were identified through database (PubMed, Medline, Ebsco, the Cochrane Library, and ScienceDirect) searching, and analysed to summarise existing evidence according to PRISMA methodology. Inclusion criteria covered original research articles reporting applications of SMI on rheumatic diseases and musculoskeletal disorders secondary to rheumatic conditions. Qualitative data synthesis was performed. Results: A total of 18 articles were included. No systematic reviews fulfilled our inclusion criteria. Most studies focused on characterising the synovial vascularity of rheumatoid arthritis. There have been several attempts to demonstrate SMI's value for evaluating extra-articular soft tissues (fat pads or salivary glands) and large-diameter vessels. The quantitative importance of SMI vascular indices could become a useful non-invasive diagnostic marker. Studies on therapeutic applications are still scarce, and the majority of studies have gaps in reporting the methodology (ultrasound performance technique and settings) of the research. Conclusions: SMI has proved to be useful in characterising low-flow vascularity, and growing evidence indicates that SMI is a non-invasive and lower-cost tool for prognostic assessment, especially in inflammatory arthritis. Preliminary findings also suggest potential interest in evaluating the effect of treatment.

Superb microvascular imaging (SMI) in the evaluation of musculoskeletal disorders: a systematic review.

OBJECTIVES: To systematically review the current literature concerning the role of superb microvascular imaging (SMI), a novel Doppler technique that enables detection of fine vessels and slow blood flow, in the evaluation of musculoskeletal disorders. METHODS: An online search of the literature was conducted for the period 2013 to April 2019 and included original articles written in English language. A data analysis was performed at the end of the literature search. RESULTS: Eight original articles with prospective design and one with retrospective design were included in this review: 4 studies focused on rheumatoid arthritis, 2 on rheumatoid and other arthritides, 1 on lateral epicondylosis and 2 on carpal tunnel syndrome. Sample size ranged from 26 to 83 patients. Despite some methodological differences, all studies compared the performance of SMI with that of a conventional Doppler technique such as power and color Doppler and found an improvement in vascularity detection with SMI. The main variations were in sample size, evaluated parameters and vascularity interpretation methods. Inter-observer agreement for SMI ranged from moderate to excellent. CONCLUSIONS: SMI is a promising tool for the diagnosis and treatment planning of different musculoskeletal disorders. Future investigations should include larger samples of patients with long-term follow-up.

Detection of microvascular damage of membranous nephropathy by MicroFlow imaging: a novel ultrasound technique.

Background: MicroFlow imaging (MFI) is a novel noninvasive ultrasound (US) technique that depicts microcirculatory blood vessels in the kidney while filtering out tissue motion and enhancing blood flow signals. We aimed to investigate the value of MFI for the detection of renal microvascular perfusion in chronic kidney disease caused by stage I-II membranous nephropathy (MN). Methods: Seventy-six participants including biopsy-proven MN (n=38) and healthy volunteers (n=38) were prospectively examined using MFI from March 2020 to December 2020. In addition, patients with MN were subdivided into a mild group, a moderate group, and a severe group based on the results of vascular pathology evaluation. All MFI images were analyzed by Image Pro Plus to obtain a cortical vascular index (VI). Basic patient information, relative US parameters and laboratory results were then acquired for each participant. Finally, after the univariate analysis among multiple groups, binary logistic regression (forward LR) and ordered logistic regression were used for multivariate analysis. Significance was set at P<0.05. Results: VI was significantly lower in MN patients compared with that of healthy controls (0.65±0.09 vs. 0.35±0.18, P<0.001). After multivariate analysis, we found that the exploratory diagnostic performance of VI [area under the curve (AUC): 0.94; 95% confidence interval (CI): 0.89-0.99] outperformed that of serum creatinine (Scr) (AUC: 0.87; 95% CI: 0.79-0.95) in identifying MN. We also observed considerable differences among MN groups in parameters including VI (P=0.006), estimated glomerular filtration rate (eGFR) (P=0.037), shape (P=0.013), and impression (P=0.007). In addition, in the group with mild vascular damage, the exploratory diagnostic performance of VI (AUC: 0.79; 95% CI: 0.64-0.94) was better than other parameters, such as eGFR (AUC: 0.63; 95% CI: 0.43-0.84). Conclusions: MFI detected abnormal renal microvascular perfusion in patients with MN (particularly in those with early vascular damage or preserved renal function) without the use of a contrast agent. Combining MFI with B-mode US can improve the predictive performance of traditional kidney US.

Ultrasound characteristics of carotid web.

BACKGROUND AND PURPOSE: Carotid web (CaW) is a cause of recurrent ischemic stroke that remains underdiagnosed using Duplex ultrasound (DUS). Improved methods and description of its ultrasound's features could allow better detection of CaW. Ultrasound microflow imaging (MFI) is a blood flow imaging technique sensitive to slow flow that could increase CaW detection. This study aimed to describe ultrasound features of CaW using B-mode imaging and MFI. METHODS: In a retrospective monocentric study, patients with CaW on CT angiography who underwent DUS examination of carotid arteries were included. DUS was performed by two nonblinded experienced neurosonologists. The specificity of CaW ultrasound features was evaluated using a group of patients with carotid atherosclerotic plaque (AP). RESULTS: Twenty-four patients with CaW were included. Mean age (standard deviation) was 48 years (11). Seventeen (71%) were females. Fifteen (63%) CaWs were symptomatic. MFI was available for 22 patients. B-mode imaging demonstrated the characteristic CaW appearance in 19/24 (79%) patients as a protruding triangular iso-hypoechoic lesion on longitudinal view. CaW were detected on axial view in only 9/24 (38%) patients. MFI displayed slow blood flow above CaW during systole and allowed it delineation, appearing as a thin triangular endoluminal defect in 18/22 (82%) cases. Based on MFI and B-mode, 21/22 (95%) CaWs were visible, including three CaWs only with MFI. These ultrasound features were not found among 24 patients with AP. CONCLUSION: We report the ultrasound features from a series of 24 CaW. The use of MFI in addition to B-mode imaging improved the detection rate of CaW.

Innovations in Vascular Ultrasound.

There are several vascular ultrasound technologies that are useful in challenging diagnostic situations. New vascular ultrasound applications include directional power Doppler ultrasound, contrast-enhanced ultrasound, B-flow imaging, microvascular imaging, 3-dimensional vascular ultrasound, intravascular ultrasound, photoacoustic imaging, and vascular elastography. All these techniques are complementary to Doppler ultrasound and provide greater ability to visualize small vessels, have higher sensitivity to detect slow flow, and better assess vascular wall and lumen while overcoming limitations color Doppler. The ultimate goal of these technologies is to make ultrasound competitive with computed tomography and magnetic resonance imaging for vascular imaging.

Microflow Imaging Analyses Reflect Mechanisms of Aggregate Formation: Comparing Protein Particle Data Sets Using the Kullback-Leibler Divergence.

Subvisible particles in therapeutic protein formulations are an increasing manufacturing and regulatory concern because of their potential to cause adverse immune responses. Flow imaging microscopy is used extensively to detect subvisible particles and investigate product deviations, typically by comparing imaging data using histograms of particle descriptors. Such an approach discards much information and requires effort to interpret differences, which is problematic when comparing many data sets. We propose to compare imaging data using the Kullback-Leibler divergence, an information theoretic measure of the difference of distributions (Kullback S, Leibler RA. 1951. Ann Math Stat. 22:79-86). We use the divergence to generate scatter plots representing the similarity between data sets and to classify new data into previously determined categories. Our approach is multidimensional, automated, and less biased than traditional techniques. We demonstrate the method with FlowCAM® imagery of protein aggregates acquired from monoclonal antibody samples subjected to different stresses. The method succeeds in classifying aggregated samples by stress condition and, once trained, is able to identify the stress that caused aggregate formation in new samples. In addition to potentially detecting subtle incipient manufacturing faults, the method may have applications to verification of product uniformity after manufacturing changes, identification of counterfeit products, and development of closely matching bio-similar products.

Calculating the mass of subvisible protein particles with improved accuracy using microflow imaging data.

Although formation of subvisible particles (1-100 µm) during manufacturing and/or storage is a major stability concern with protein therapeutics, particle numbers are often too low to permit for direct experimental measurement of their protein content (mass). The objective of this work was to develop a novel, accurate, and easy-to-implement method to calculate the mass of subvisible protein particles using particle number, size, and morphology data obtained from microflow imaging (MFI) measurements. The method was evaluated using (1) spherical and nonspherical polystyrene standards and (2) shake and stir-stressed IgG1 mAb solutions. For extensively stressed mAb samples, in which protein mass loss after particle removal could be measured experimentally, calculated results were in good agreement and showed improvements in accuracy and precision compared with other methods. Improved estimates of protein mass in particles were made possible by using morphological data to better model particle volume, and by using literature-based values for protein density and particle composition. This method improves estimations of protein particle mass when total amounts are too low to be measured experimentally and also facilitates a better understanding of protein particle formation by accounting for particle mass as well as number.

Label-free flow cytometry analysis of subvisible aggregates in liquid IgG1 antibody formulations.

The objective of this study was to characterize and quantify label-free subvisible antibody particles in different formulations based on their size and physical properties by flow cytometry. Protein subvisible particles were prepared under various stress conditions and analyzed by applying different analytical techniques [light obscuration (LO), microflow imaging (MFI), and flow cytometry (FACS)] for the detection of aggregates. The capability of the FACS method to detect and count subvisible particles was evaluated and benchmarked against conventional techniques. FACS can analyze particles down to 500 nm reducing the gap between size-exclusion chromatography and LO. The applied methods of FACS, LO, and MFI displayed a proportional correlation between the total particle counts, however, FACS can provide additional information on the structural characteristics of such aggregated particles.

The effect of protein PEGylation on physical stability in liquid formulation.

The presence of micron aggregates in protein formulations has recently attracted increased interest from regulatory authorities, industry, and academia because of the potential undesired side effects of their presence. In this study, we characterized the micron aggregate formation of hen egg-white lysozyme (Lyz) and its diPEGylated (5 kDa) analog as a result of typical handling stress conditions. Both proteins were subjected to mechanical stress in the absence and presence of silicone oil (SO), elevated temperatures, and freeze-thaw cycles. Flow imaging microscopy showed that PEGylated Lyz formed approximately half as many particles as Lyz, despite its lower apparent thermodynamic stability and more loose protein fold. Further characterization showed that the PEGylation led to a change from attractive to repulsive protein-protein interactions, which may partly explain the reduced particle formation. Surprisingly, the PEGylated Lyz adsorbed an order of magnitude faster onto SO, despite being much larger in size, as determined by small-angle X-ray scattering and dynamic light scattering measurements. Thus, PEGylation may significantly reduce, but not prevent, micron aggregate formation of a protein during typical handling stresses.

Quality control of protein crystal suspensions using microflow imaging and flow cytometry.

Protein crystallization is an attractive method for protein processing and formulation. However, minor changes in the crystallization setup can lead to changes in the crystal structure or the formation of amorphous protein aggregates, which affect the product quality. Only few analytical tools for qualitative and quantitative differentiation between protein crystals and amorphous protein exist. Electron microscopy requires expensive instrumentation, demanding sample preparation, and challenging image analysis. Therefore, there is a need to establish other analytical techniques. It was the aim of this study to investigate the capability of light obscuration (LO), microflow imaging (MFI), and flow cytometry (FC) in differentiating the amorphous and crystalline states of insulin as a relevant model. Qualitative discrimination of the two populations based on the particle size was possible using LO. Quantitative determination of amorphous protein and crystals by MFI was challenging due to overlapping size distributions. This problem was overcome by particle analysis based on the mean light intensity. Additionally, FC was applied as a new method for the determination of the quality and quantity of amorphous protein by differences in the light scattering. Our results show the potential of MFI and FC for rapid high throughput screening of crystallization conditions and product quality.

Radar chart array analysis to visualize effects of formulation variables on IgG1 particle formation as measured by multiple analytical techniques.

This study presents a novel method to visualize protein aggregate and particle formation data to rapidly evaluate the effect of solution and stress conditions on the physical stability of an immunoglobulin G (IgG) 1 monoclonal antibody (mAb). Radar chart arrays were designed so that hundreds of microflow digital imaging (MFI) solution measurements, evaluating different mAb formulations under varying stresses, could be presented in a single figure with minimal loss of data resolution. These MFI radar charts show measured changes in subvisible particle number, size, and morphology distribution as a change in the shape of polygons. Radar charts were also created to visualize mAb aggregate and particle formation across a wide size range by combining data sets from size-exclusion chromatography, Archimedes resonant mass measurements, and MFI. We found that the environmental/mechanical stress condition (e.g., heat vs. agitation) was the most important factor in influencing the particle size and morphology distribution with this IgG1 mAb. Additionally, the presence of NaCl exhibited a pH and stress-dependent behavior resulting in promotion or inhibition mAb particle formation. This data visualization technique provides a comprehensive analysis of the aggregation tendencies of this IgG1 mAb in different formulations with varying stresses as measured by different analytical techniques.

Application of different analytical methods for the characterization of non-spherical micro- and nanoparticles.

Non-spherical micro- and nanoparticles have recently gained considerable attention due to their surprisingly different interaction with biological systems compared to their spherical counterparts, opening new opportunities for drug delivery and vaccination. Up till now, electron microscopy is the only method to quantitatively identify the critical quality attributes (CQAs) of non-spherical particles produced by film-stretching; namely size, morphology and the quality of non-spherical particles (degree of contamination with spherical ones). However, electron microscopy requires expensive instrumentation, demanding sample preparation and non-trivial image analysis. To circumvent these drawbacks, the ability of different particle analysis methods to quantitatively identify the CQA of spherical and non-spherical poly(1-phenylethene-1,2-diyl (polystyrene) particles over a wide size range (40 nm, 2 µm and 10 µm) was investigated. To this end, light obscuration, image-based analysis methods (Microflow imaging, MFI, and Vi-Cell XR Coulter Counter) and flow cytometry were used to study particles in the micron range, while asymmetric flow field fractionation (AF4) coupled to multi-angle laser scattering (MALS) and quasi elastic light scattering (QELS) was used for particles in the nanometer range, and all measurements were benchmarked against electron microscopy. Results show that MFI can reliably identify particle size and aspect ratios of the 10 µm particles, but not the 2 µm ones. Meanwhile, flow cytometry was able to differentiate between spherical and non-spherical 10 or 2 µm particles, and determine the amount of impurities in the sample. As for the nanoparticles, AF4 coupled to MALS and QELS allowed the measurement of the geometric (rg) and hydrodynamic (rh) radii of the particles, as well as their shape factors (rg/rh), confirming their morphology. While this study shows the utility of MFI, flow cytometry and AF4 for quantitative evaluation of the CQA of non-spherical particles over a wide size range, the limitations of the methods are discussed. The use of orthogonal characterization methods can provide a complete picture about the CQA of non-spherical particles over a wide size range.

Detection of prostate cancer with contrast-enhanced ultrasonographic microflow imaging: comparison with conventional ultrasonography / 中华超声影像学杂志

ObJective To evaluate the value of contrast-enhanced ultrasonography microflow imaging (MFI) in detecting prostate cancer. Methods Sixty-five patients with serum prostate-specific antigen levels higher than 4.00 μg/L were evaluated with transrectal gray-scale,power Doppler,and MFI ultrasonography and then biopsy guided by ultrasonography. Biopsy was performed at twelve sites in the base,the mid gland and the apex in each patient. In these three transverse sections, when any of the three methods showed abnormality,the biopsy site was directed to the abnormal foci. Diagnostic efficiency of the three methods for prostate cancer detection was compared based on biopsy results according to patient and biopsy site. Results Overall prostate cancers were detected in 230 (29.5 %) of 780 specimens in 36(55.4%) of 65 patients. MFI could detect more patients(34) than gray-scale(26) and power Doppler(28) (P = 0.021, P = 0.031), 6(16.7%)of the 36 patients diagnosed with cancer were identified only by MFI. By biopsy site, MFI had higher sensitivity and overall accuracy (80.0% and 83.0%) than gray-scale (47.0% and 76.8%) and power Doppler (37.4% and 74.6%) ultrasonography(P <0.001, P<0.001 ; P = 0.001, P <0.001), while the specificity of MFI was 84.4%, lower than gray-scale (89.3%) and power Doppler (90.2%) ultrasonography(P = 0.009, P < 0.001). Conclusions MFI could detect more patients and improve sensitivity and overall accuracy by biopsy site than conventional uhrasonography.