Automated Image Analysis Software Research Articles

The Global Track Concept for Assessment of Engaging Hill-Sachs Defects in Anterior Shoulder Instability.

The glenoid track concept is used to determine preoperatively whether a Hill-Sachs defect is engaging or not. Currently, the glenoid track concept relies on measurements of bony structures as well as on the confines and elasticity of the rotator cuff as a reference point, which varies extensively among individuals and therefore limits the reliability and accuracy of this concept. To evaluate the reliability of the global track concept, which determines the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head as a new reference point with the help of an automated image analysis software and 3-dimensional analysis of the humeral head. Controlled laboratory study. Computed tomography scans of 100 patients treated for anterior shoulder instability with different sizes of Hill-Sachs defects were evaluated manually by 2 orthopaedic surgeons independently using the software OsiriX as well as automatically by using a dedicated prototype software (ImFusion). Obtained manual and automated measurements included the Hill-Sachs length, Hill-Sachs width, and Hill-Sachs depth of the defect; the Hill-Sachs interval (HSI); and the glenoid width for the glenoid track concept, as well as the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head (global track concept). The reliability of the different measurement techniques was compared by calculating intraclass correlation coefficients (ICCs). There was a significant difference for all obtained parameters comparing manual and automatic measurements. For manually obtained parameters, measurements referring to bony boundaries (glenoid width, Hill-Sachs length, and Hill-Sachs width) showed good to excellent agreement (ICC, 0.86, 0.82, and 0.62, respectively), while measurements referring to soft tissue boundaries (HSI and glenoid track; ICC, 0.56 and 0.53, respectively) or not directly identifiable reference points (center of articular surface and global track) only showed fair reliability (ICC middle excursion, 0.42). When the same parameters were measured with the help of an automated software, good reliability for the glenoid track concept and excellent reliability for the global track concept in the middle excursion were achieved. The present study showed that the more complex global track measurements of humeral defects are more reliable than the current standard HSI and glenoid track measurements. However, this is only true when automated software is used to perform the measurements. Future studies using the new proposed method in combination with an automated software need to be conducted to determine critical threshold values for defects prone to engagement.

An adaptive framework of De-noising and enhancement for fundus imaging using enhanced guided filter and non-illumination correction method for diabetic retinopathy

Retinal fundus imaging has been used in the diagnosis of many cardiovascular and retinal-related diseases like age-related macular degeneration, pathological myopia, diabetic retinopathy, and glaucoma. In recent years, the computerized technique is adapted to fundus image analysis and processing for quick diagnosis. However, low-quality retinal pictures are produced when certain eye disorders and photographic circumstances exist. Poor-quality retinal pictures of this kind are not helpful for diagnosis, particularly when using automated image analysis software. The causes for degradation include low illumination, high illumination, image blurring, uneven illumination, low contrast, and color distortion. Several image-enhancing techniques have been developed to address this; however, they may result in sudden changes in color levels, false borders, and the loss of picture detail, particularly in retinal images. To prevent these negative consequences, this paper proposed a novel enhancement algorithm EGF_AGC (Enhanced Guided Filter+Adaptive Gamma Correction) for color fundus retinal images. The proposed method is worked is described in four steps. The first stage of the recommended procedure is to utilize a hybrid filter to enhance the retinal pictures’ appearance details. Next, applied enhanced guided filter method to the detailed retina images to enhance the image quality. Moreover, the suggested technique also worked to retain uneven illumination issues raised in retina images using the Adaptive Gamma Correction method. The final improved retinal pictures are created by combining the phases before utilizing the HSV color approach. The databases of retinal images, which include the STARE, the DRIVE, and the CHBASE_DB1 database, were utilized to determine image enhancement effects. The findings were compared with CLAHE, Bilateral Filter, Guided Filter, and Gamma Correction retinal enhancement methods. Compared to similar enhancement approaches, experiments showed that our method could give a competitive outcome and eliminate degradation caused by low-quality fundus pictures.

Automated CT perfusion analysis reveals medial temporal perfusion abnormalities during transient global amnesia

IntroductionThe underlying pathophysiology of Transient global amnesia (TGA) remains elusive. Reports of perfusion abnormalities in TGA were inconsistent, but semi-automated analysis of perfusion CT (CTP) may improve reliability and precision of perfusion deficit detection. MethodsPer institutional protocol, all TGA patients undergo multiphasic contrast-CT with arch to vertex CT angiography, intracranial CT venography, MRI, and EEG upon admission. During the study period consecutive patients diagnosed with TGA underwent CTP during the early acute amnestic phase. We retrospectively reviewed the clinical and radiological findings. ResultsFive patients (3 female. median age 71, range 47–74) fulfilled entry criteria. Automated CTP analysis revealed the absence of an ischemic core (defined by CBF < 30%) or conventionally defined clinically relevant hypoperfusion area (defined by Time-to-maximum (Tmax) >6 s) in any of the patients. However, four of the five patients demonstrated territories of benign oligemia defined as Tmax>4 s in areas supplied by the Posterior Cerebral Artery. Three of these four patients had clear involvement of the bilateral medial temporal lobes. None of the patients had epileptic activity on their EEG. Both CTA and MRI were normal apart for small foci of restricted diffusion in the hippocampus of four patients. DiscussionDeficits in perfusion were found in the hippocampi of 60% of patients in the acute phase of TGA using automated image analysis software. This method may provide a quick and simple method to detect these abnormalities. These perfusion abnormalities could help solidify the diagnosis at an early stage and may advance our understanding of this elusive syndrome.

Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp

Sarcomeres are the basic contractile units within cardiac myocytes, and the collective shortening of sarcomeres aligned along myofibrils generates the force driving the heartbeat. The alignment of the individual sarcomeres is important for proper force generation, and misaligned sarcomeres are associated with diseases, including cardiomyopathies and COVID-19. The actin bundling protein, α-actinin-2, localizes to the ‘Z-Bodies” of sarcomere precursors and the ‘Z-Lines’ of sarcomeres, and has been used previously to assess sarcomere assembly and maintenance. Previous measurements of α-actinin-2 organization have been largely accomplished manually, which is time-consuming and has hampered research progress. Here, we introduce sarcApp, an image analysis tool that quantifies several components of the cardiac sarcomere and their alignment in muscle cells and tissue. We first developed sarcApp to utilize deep learning-based segmentation and real space quantification to measure α-actinin-2 structures and determine the organization of both precursors and sarcomeres/myofibrils. We then expanded sarcApp to analyze ‘M-Lines’ using the localization of myomesin and a protein that connects the Z-Lines to the M-Line (titin). sarcApp produces 33 distinct measurements per cell and 24 per myofibril that allow for precise quantification of changes in sarcomeres, myofibrils, and their precursors. We validated this system with perturbations to sarcomere assembly. We found perturbations that affected Z-Lines and M-Lines differently, suggesting that they may be regulated independently during sarcomere assembly.

Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp.

Sarcomeres are the basic contractile units within cardiac myocytes, and the collective shortening of sarcomeres aligned along myofibrils generates the force driving the heartbeat. The alignment of the individual sarcomeres is important for proper force generation, and misaligned sarcomeres are associated with diseases, including cardiomyopathies and COVID-19. The actin bundling protein, α-actinin-2, localizes to the 'Z-Bodies" of sarcomere precursors and the 'Z-Lines' of sarcomeres, and has been used previously to assess sarcomere assembly and maintenance. Previous measurements of α-actinin-2 organization have been largely accomplished manually, which is time-consuming and has hampered research progress. Here, we introduce sarcApp, an image analysis tool that quantifies several components of the cardiac sarcomere and their alignment in muscle cells and tissue. We first developed sarcApp to utilize deep learning-based segmentation and real space quantification to measure α-actinin-2 structures and determine the organization of both precursors and sarcomeres/myofibrils. We then expanded sarcApp to analyze 'M-Lines' using the localization of myomesin and a protein that connects the Z-Lines to the M-Line (titin). sarcApp produces 33 distinct measurements per cell and 24 per myofibril that allow for precise quantification of changes in sarcomeres, myofibrils, and their precursors. We validated this system with perturbations to sarcomere assembly. We found perturbations that affected Z-Lines and M-Lines differently, suggesting that they may be regulated independently during sarcomere assembly.

Root Walker: an automated pipeline for large scale quantification of early root growth responses at high spatial and temporal resolution.

Plants are sessile organisms that constantly adapt to their changing environment. The root is exposed to numerous environmental signals ranging from nutrients and water to microbial molecular patterns. These signals can trigger distinct responses including the rapid increase or decrease of root growth. Consequently, using root growth as a readout for signal perception can help decipher which external cues are perceived by roots, and how these signals are integrated. To date, studies measuring root growth responses using large numbers of roots have been limited by a lack of high-throughput image acquisition, poor scalability of analytical methods, or low spatiotemporal resolution. Here, we developed the Root Walker pipeline, which uses automated microscopes to acquire time-series images of many roots exposed to controlled treatments with high spatiotemporal resolution, in conjunction with fast and automated image analysis software. We demonstrate the power of Root Walker by quantifying root growth rate responses at different time and throughput scales upon treatment with natural auxin and two mitogen-associated protein kinase cascade inhibitors. We find a concentration-dependent root growth response to auxin and reveal the specificity of one MAPK inhibitor. We further demonstrate the ability of Root Walker to conduct genetic screens by performing a genome-wide association study on 260 accessions in under 2 weeks, revealingknown and unknown root growth regulators. Root Walker promises to be a useful toolkit for the plant science community, allowing large-scale screening of root growth dynamics for a variety of purposes, including genetic screens for root sensing and root growth response mechanisms.

Region specific reduction of neurogranin immunostaining in the hippocampus of Alzheimer’s disease brains

AbstractBackgroundNeurogranin (Ng) is a post‐synaptic protein involved in formation of long‐term potentials, and thus plays an important role in learning and memory. The concentration of Ng is increased in the cerebrospinal fluid (CSF) of people with Alzheimer’s disease (AD), and CSF Ng is considered to be a biomarker for synaptic dysfunction in AD. The expression of Ng protein in the human brain and its association with AD pathology, however, are less well studied.MethodUsing immunohistochemical detection methods we investigated the protein expression of Ng, beta‐amyloid (Aβ) and hyperphosphorylated paired helical filament tau (phospho‐tau; ptau) in the post‐mortem hippocampus from 12 AD patients and 12 cognitively unaffected controls (NC). We compared the number of positively stained cells (Ng, ptau) or plaques (Aβ) in the hippocampal sub‐regions (dentate gyrus, Cornu Ammonis (CA) fields 1‐3) from the 2 groups. Donor samples with intermediate AD (i.e., Braak stage III‐IV) or NC (Braak 0‐I/II) were analyzed using Image Pro Premier automated image analysis software (version 10.01). The data were normalized to the number of cells or clusters/mm2 and analyzed using a one‐way ANOVA. Using a test of circularity (Cir = (4*A)/Pi*MaxFeret2), we further investigated morphological differences in the Ng positive neurons by measuring the Ng expression in the apical dendrite of pyramidal neurons.ResultThere was a 40% reduction of Ng immunopositive neurons in AD compared to NC in the CA3(N = 24;p = 0.0003) and in the CA1(N = 24;p = 0.014) hippocampal sub‐regions. Ng positive cells did not change significantly in AD compared to NC in the other hippocampal regions. Additionally, we showed that CA1 region, Ng was localized mainly in the neuronal cell soma of AD donors, while its expression extended into the apical dendrites of NC donors (N = 24;p = 0.006). In AD, Ng immunostaining negatively correlated with ptau immunopositive neurofibrillary tangles (r = ‐0.70;p = 0.04)ConclusionSelective loss of Ng immunostaining in certain subregions of the hippocampus suggests that synaptic damage in AD maybe a region‐specific event. The mechanism underlying such regional synaptic damage and the changes in Ng expression will be key in understanding the selective vulnerability of brain regions to AD.

Comparison of the detectability of hot lesions on bone SPECT using six state-of-the-art SPECT/CT systems: a multicenter phantom study to optimize reconstruction parameters.

Single-photon emission computed tomography with X-ray computed tomography (SPECT/CT) systems have diversified due to the remarkable developments made by each manufacturer. This study aimed to optimize the reconstruction parameters of six state-of-the-art SPECT/CT systems and compare their image quality of bone SPECT.SPECT images were acquired on SPECT/CT systems, including Symbia Intevo, Discovery NM/CT 670, Discovery NM/CT 870 CZT, Brightview XCT, and VERITON-CT. SIM2 bone phantom with tough lung phantoms on both sides of the spinal inserts that simulate the thorax was used for image quality assessment. SPECT images were obtained at individual workstations using an ordered subset expectation maximization method with three-dimensional resolution recovery, as well as CT attenuation and scatter correction, subset 2, iteration 12-84, and a full width at half maximum 10-mm Gaussian smooth filter. An automatic image analysis software dedicated to SIM2 bone phantom was used to assess the contrast-to-noise ratio (CNR), relative recovery coefficient, percentage of coefficient of variance, contrast, and detectability. The optimal parameters for each system were defined with superior detectability of spherical lesions and noise characteristics, as well as the highest CNR.All systems exhibited better image quality indexes using the optimal parameters than using the manufacturer's recommended parameters. The detectability of all systems was in agreement while using the optimal parameters.Detectability agreement can be achieved by optimizing the reconstruction parameters for different reconstruction algorithms, which can further improve the image quality. Therefore, future research should focus on optimal reconstruction parameters for SPECT alone.

AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds

Seed quality is the key to seedling development, which affects field yield. Researchers, breeders, and processors may need to investigate seed quality on a large scale, which requires high-throughput methods, which are currently lacking. To address this bottleneck, we have developed a novel software named AIseed that automatically captures and analyzes great traits, such as the shape, color, or texture features of individual seeds, which are rarely effortlessly and stably determined by conventional measurements. The AIseed software is based on machine vision technology and enables high-throughput handling and phenotyping of various-sized plant seeds. After phenotyping for a total of 54 features, AIseed numbers each seed to facilitate further seed quality assessment or grain detection. In addition, AIseed includes modules for seed quality detection and prediction, such as seed clarity, purity, vigor, and viability testing, which are based on machine learning or deep learning models developed by analyzing the associations between seed quality indicators and the features obtained by the software. Through a series of experiments, this paper has confirmed that AIseed has a high performance in extracting phenotypic features and testing seed quality from images for seeds of several plants of different sizes, with high speed and high precision in separating seeds from the background and then analyzing them. In summary, it is a non-destructive and highly effective software for accurate seed phenotyping and seed quality assessment, which is essential for both breeding and yield improvement.

Deep-learning applications in the automated image analysis of giant unilamellar vesicles.

Recent progress in the field of machine learning allowed scientists to develop software to analyze a great amount of image data efficiently. Trained artificial neural networks can segment, analyze, and classify the target objects in complex images. Face recognition technology in our cell phones is an everyday experience now. We have been developing automated image analysis software to analyze lipid vesicle images efficiently. Giant unilamellar vesicles (GUV), characterized by a diameter greater than 1 µm are routinely used for reconstitution studies. They are used to study a wide range of physiological activities involving lipid membranes. One of the great advantages of the GUV system is that they can be readily imaged, and studied by optical microscopy due to their size much greater than the resolution of visible light's diffraction limit. Fluorescence microscopy image analysis can reveal the protein-lipid interaction, phase separation on the membranes, and mechanical deformation of the membranes. Such analysis typically involves the manual analysis by researchers to identify the target vesicles to analyze and perform quantitative analysis. Our approach to introducing deep-learning can automate the process to help researchers to analyze a great amount of vesicle image data efficiently. Our work shows that with some training, scientists can introduce the deep-learning as a module for their image analysis for various purposes. We discuss the progress in the field of software analysis of vesicle images and show our example works of deep learning applications. Deep-learning assisted identification, and classification of vesicle states and their connection with quantitative analysis will be shown. We suggest practical ways to implement it for various purposes of image analysis. We envision such modules can be potentially introduced in the classroom to teach future scientists to learn machine learning applications in biophysics.

Nanofibers are a matter of perspective: effects of methodology and subjectivity on diameter measurements.

Nanofibers are currently among the most researched nanomaterials in materials science. Various high-resolution microscopy techniques are used for morphological investigations, with the diameter as primary characteristic. Since methodological factors influencing the diameter distribution are usually ignored, numerical values can hardly be compared across different or even within single studies. Here, we investigate influencing factors such as microscopy technique, degree of magnification, eventual coatings, and the analysts' bias in the image selection and evaluation. We imaged a single nanofiber sample using scanning electron microscopy (SEM), helium ion microscopy (HIM), atomic force microscopy (AFM), and transmission electron microscopy (TEM). These techniques yield significant methodological variations between the diameter distributions. We further observed a strong influence of analysts' subjectivity, with a consistent average deviation between 4 different analysts of up to 31%. The average deviation between micrographs within each category was 14%, revealing a considerable influence of micrograph selection and strong potential for cherry picking. The mean values were mostly comparable with the results using automated image analysis software, which was more reproducible, much faster, and more accurate for images with lower magnification. The results demonstrate that one of the most frequently measured characteristics of nanofibers is subject to strong systematic fluctuations that are rarely if ever addressed.

Improved methods for quantifying soil invertebrates during ecotoxicological tests: Chill comas and anesthetics

Soil invertebrate ecotoxicological tests are important when making informed site-management decisions. However, traditional tests are time-consuming and require quantification of high numbers of soil invertebrates burrowed beneath the surface of soil. A commonly used technique to extract invertebrates from the soil is the floatation method. Due to the movement of Collembola, and the presence of small soil particulates and bubbles on the surface of the water, automatic image analysis software may inaccurately quantify the true number of individuals present. Hence, manual counting immediately following extraction, or from images, is still the most effective method utilized for quantifying floated soil invertebrates. This study investigated three novel techniques; the use of an ice-water bath, chest freezer (−12 °C) and ethanol to temporarily immobilize groups of 35 Folsomia candida individuals to increase accuracy during the quantification step. Active thermography to aid automatic image analysis was also investigated. Results show that while thermoimaging did not provide a distinct advantage in differentiating soil invertebrates from soil particles, both an ice-water bath and 4.75% ethanol solution were extremely effective at temporarily immobilizing F. candida with no apparent ill effects. The outcome of this study will assist future soil invertebrate research by increasing the accuracy of invertebrate quantifications. In addition, as the techniques caused no mortality to the invertebrates, the same individuals remain available for continuous monitoring experiments, repeated exposure, and/or multi-generational studies.

Automated whole slide image analysis for a translational quantification of liver fibrosis

Current literature highlights the need for precise histological quantitative assessment of fibrosis which cannot be achieved by conventional scoring systems, inherent to their discontinuous values and reader-dependent variability. Here we used an automated image analysis software to measure fibrosis deposition in two relevant preclinical models of liver fibrosis, and established correlation with other quantitative fibrosis descriptors. Longitudinal quantification of liver fibrosis was carried out during progression of post-necrotic (CCl4-induced) and metabolic (HF-CDAA feeding) models of chronic liver disease in mice. Whole slide images of picrosirius red-stained liver sections were analyzed using a fully automated, unsupervised software. Fibrosis was characterized by a significant increase of collagen proportionate area (CPA) at weeks 3 (CCl4) and 8 (HF-CDAA) with a progressive increase up to week 18 and 24, respectively. CPA was compared to collagen content assessed biochemically by hydroxyproline assay (HYP) and by standard histological staging systems. CPA showed a high correlation with HYP content for CCl4 (r = 0.8268) and HF-CDAA (r = 0.6799) models. High correlations were also found with Ishak score or its modified version (r = 0.9705) for CCl4 and HF-CDAA (r = 0.9062) as well as with NASH CRN for HF-CDAA (r = 0.7937). Such correlations support the use of automated digital analysis as a reliable tool to evaluate the dynamics of liver fibrosis and efficacy of antifibrotic drug candidates in preclinical models.

Combination of the Focus-Forming Assay and Digital Automated Imaging Analysis for the Detection of Dengue and Zika Viral Loads in Cultures and Acute Disease.

Optimized methods for the detection of flavivirus infections in hyperendemic areas are still needed, especially for working with patient serum as a starting material. The focus-forming assay (FFA) reveals critical aspects of virus-host interactions, as it is a quantitative assay to determine viral loads. Automated image analysis provides evaluations of relative amounts of intracellular viral protein at the single-cell level. Here, we developed an optimized FFA for the detection of infectious Zika virus (ZIKV) and dengue virus (DENV) viral particles in cell cultures and clinical serum samples, respectively. Vero-76 cells were infected with DENV-2 (16681) or ZIKV (PRVA BC59). Using a panel of anti-DENV and anti-ZIKV NS1-specific monoclonal antibodies (mAbs), the primary mAbs, concentration, and the optimal time of infection were determined. To determine whether intracellular accumulation of NS1 improved the efficiency of the FFA, brefeldin A was added to the cultures. Focus formation was identified by conventional optical microscopy combined with CellProfiler™ automated image analysis software. The FFA was used with spike assays for ZIKV and clinical specimens from natural infection by DENV-1 and DENV-2. mAb 7744-644 for ZIKV and mAb 724-323 for DENV used at a concentration of 1 μg/ml and a time of 24 hours postinfection produced the best detection of foci when combining conventional counting and automated digital analysis. Brefeldin A did not improve the assessment of FFUs or their digitally assessed intensity at single-cell level. The FFA showed 95% ZIKV recovery and achieved the detection of circulating DENV-1 and DENV-2 in the plasma of acutely ill patients. The combination of the two techniques optimized the FFA, allowing the study of DENV and ZIKV in culture supernatants and clinical specimens from natural infection in hyperendemic areas.

Circulating tumor cells as a biomarker for monitoring: Disease progression, treatment response, and minimal residual disease.

e15021 Background: To analyze the role of Circulating tumor cells (CTC) as a confirmatory personalized biomarker for monitoring the disease progression, disease burden, and minimal residual disease in epithelial origin cancers. Methods: In this retrospective study, 127 patients with colorectal, breast, and ovarian cancer at stage III and IV were analyzed. The patients were at various stages of intensive chemo and radiotherapy while the CTCs were isolated and enumerated from 1.5 ml of blood. The decision to continue chemotherapy or change to oral metronomic therapy was based on the presence of circulating tumor cells in Stage III. While in stage IV, serial measurement of CTC guided therapy. CTCs were isolated using OncoDiscover platform possessing EpCAM antibody based immunomagnetic targeting of magnetic nanoparticles after RBC lysis. CTCs were imaged and identified as CK18+ and CD45- cells showing a well-defined nucleus using a motorized fluorescence microscope operational with a monochrome camera. CTCs were enumerated using automated image analysis software and counts were expressed as number per 1.5 ml of blood. Results: In this retrospective study we analyzed blood sample from 127 patients with the advanced stage epithelial cancers (breast- 50 %, ovarian -27 %, colorectal- 23 %) for the presence of CTCs. Amongst those, 52 % showed the presence of CTCs (breast- 52 %, ovarian -46 %, colorectal- 58 %). The CTC count ranged between 1-5 / 1.5 ml of blood with mean and median value of 2 and 1. Among the CTC positive population, majority had CTC count of 1 (44.4 %), while more than 2 CTCs were observed in 11 % of population. CTC clusters were detected in 13 % of population which predominantly were stage IV patients. 67 % among the follow up patients showed decrease in CTC count from the baseline due to the prescribed treatment, while 22 % patients showed increase in CTC count from the baseline. 11 % patients did not show change in CTC count from the baseline. When CTCs count was investigated as an independent variable to monitor the therapeutic response, it correlated well with the positive or negative outcome. In few representative cases, the reduction of CTC number from the basal value was indicative of an effective treatment. Exceptionally, in a representative colorectal cancer case, PET showed no primary as well secondary tumor burden, but the presence of CTCs in blood led further investigating an abdominal MRI that indicated multiple liver lesions suggesting micro-metastasis. Subsequent to SIRT treatment, the patient showed complete tumor regression and absence of CTCs in peripheral blood. Conclusions: Our data suggest that CTC can serve as a dynamic intermittent biomarker for monitoring the disease progression in advanced stages and assess the therapeutic response, thus emphasizing the role of CTCs in personalized cancer management.

Automated cross-sectional analysis of trained, severely atrophied, and recovering rat skeletal muscles using MyoVision 2.0.

The number of myonuclei within a muscle fiber is an important factor in muscle growth, but its regulation during muscle adaptation is not well understood. We aimed to elucidate the time course of myonuclear dynamics during endurance training, loaded and concentric resistance training, and nerve silencing-induced disuse atrophy with subsequent recovery. We modified tibialis anterior muscle activity in free-living rats with electrical stimulation from implantable pulse generators, or with implantable osmotic pumps delivering tetrodotoxin (TTX) to silence the motor nerve without transection. We used the updated, automated software MyoVision to measure fiber-type-specific responses in whole tibialis anterior cross sections (∼8,000 fibers each). Seven days of continuous low-frequency stimulation (CLFS) reduced muscle mass (-12%), increased slower myosin isoforms and reduced IIX/IIB fibers (-32%), and substantially increased myonuclei especially in IIX/IIB fibers (55.5%). High-load resistance training (spillover) produced greater hypertrophy (∼16%) in muscle mass and fiber cross-sectional area (CSA) than low-load resistance training (concentric, ∼6%) and was associated with myonuclear addition in all fiber types (35%-46%). TTX-induced nerve silencing resulted in progressive loss in muscle mass, fiber CSA, and myonuclei per fiber cross section (-50.7%, -53.7%, and -40.7%, respectively, at 14 days). Myonuclear loss occurred in a fiber-type-independent manner, but subsequent recovery during voluntary habitual activity suggested that type IIX/IIB fibers contained more new myonuclei during recovery from severe atrophy. This study demonstrates the power and accuracy provided by the updated MyoVision software and introduces new models for studying myonuclear dynamics in training, detraining, retraining, repeated disuse, and recovery.NEW & NOTEWORTHY We introduce new models for studying fiber-type-specific myonuclear dynamics in muscle training, detraining, retraining, disuse, and recovery. We show that the various fiber types do not respond identically and that myonuclear number changes during adaptation. We also critically assess an updated version of MyoVision automated image analysis software to quantify whole muscle immunofluorescent microscopical images in a faster and less computer intensive manner. MyoVision remains open source and freely available with more user-controlled features.

Sarcopenia Is a Clinically Relevant and Independent Predictor of Health Outcomes after Chimeric Antigen Receptor T-Cell Therapy for Lymphoma

Sarcopenia Is a Clinically Relevant and Independent Predictor of Health Outcomes after Chimeric Antigen Receptor T-Cell Therapy for Lymphoma

Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons

Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.

Immune Responses Vary in Preinvasive Colorectal Lesions by Tumor Location and Histology.

Immune responses vary in colorectal cancers, which strongly influence prognosis. However, little is known about the variance in immune response within preinvasive lesions. The study aims to investigate how the immune contexture differs by clinicopathologic features (location, histology, dysplasia) associated with progression and recurrence in early carcinogenesis. We performed a cross-sectional study using preinvasive lesions from the surgical pathology laboratory at the Medical University of South Carolina. We stained the tissues with immunofluorescence antibodies, then scanned and analyzed expression using automated image analysis software. We stained CD117 as a marker of mast cells, CD4/RORC to indicate Th17 cells, MICA/B as a marker of NK-cell ligands, and also used antibodies directed against cytokines IL6, IL17A, and IFNγ. We used negative binomial regression analysis to compare analyte density counts by location, histology, degree of dysplasia adjusted for age, sex, race, and batch. All immune markers studied (except IL17a) had significantly higher density counts in the proximal colon than distal colon and rectum. Increases in villous histology were associated with significant decreases in immune responses for IL6, IL17a, NK ligand, and mast cells. No differences were observed in lesions with low- and high-grade dysplasia, except in mast cells. The lesions of the proximal colon were rich in immune infiltrate, paralleling the responses observed in normal mucosa and invasive disease. The diminishing immune response with increasing villous histology suggests an immunologically suppressive tumor environment. Our findings highlight the heterogeneity of the immune responses in preinvasive lesions, which may have implications for prevention strategies. PREVENTION RELEVANCE: Our study is focused on immune infiltrate expression in preinvasive colorectal lesions; our results suggest important differences by clinicopathologic features that have implications for immune prevention research.

Critical Comparison between Large and Mini Vertical Flow Immunoassay Platforms for Yersinia Pestis Detection.

Yersinia pestis is a Gram-negative bacterium that is the causative agent of plague and is widely recognized as a potential biological weapon. Due to the high fatality rate of plague when diagnosis is delayed, the development of rapid, sensitive, specific, and cost-effective methods is needed for its diagnosis. The Y. pestis low calcium response V (LcrV) protein has been identified as a potential microbial biomarker for the diagnosis of plague. In this paper, we present a highly sensitive, paper-based, vertical flow immunoassay (VFI) prototype for the detection of LcrV and the diagnosis of plague. An antigen-capture assay using monoclonal antibodies is employed to capture and detect the LcrV protein, using a colorimetric approach. In addition, the effect of miniaturizing the VFI device is explored based on two different sizes of VFI platforms, denoted as "large VFI" and "mini VFI." Also, a comparative analysis is performed between the VFI platform and a lateral flow immunoassay (LFI) platform to exhibit the improved assay sensitivity suitable for point-of-care (POC) diagnostics. The analytical sensitivity or limit of detection (LOD) in the mini VFI is approximately 0.025 ng/mL, that is, 10 times better than that of the large VFI platform or 80 times over a standard lateral flow configuration. The low LOD of the LcrV VFI appears to be highly suitable for testing clinical samples and potentially diagnosing plague at earlier time points. In addition, optimization of the gold nanoparticle (AuNP) concentration, nanomaterial plasmonic properties, and flow velocity analysis could improve the performance of the VFI. Furthermore, we developed automated image analysis software that shows potential for integrating the diagnostic system into a smartphone. These methods and findings demonstrate that the VFI platform is a highly sensitive device for detecting the LcrV and potentially many other biomarkers.