Quantification Algorithm Research Articles

Quantification of fluorescence angiography for visceral perfusion assessment: measuring agreement between two software algorithms.

Indocyanine green fluorescence angiography (ICG-FA) may reduce perfusion-related complications of gastrointestinal anastomosis. Software implementations for quantifying ICG-FA are emerging to overcome a subjective interpretation of the technology. Comparison between quantification algorithms is needed to judge its external validity. This study aimed to measure the agreement for visceral perfusion assessment between two independently developed quantification software implementations. This retrospective cohort analysis included standardized ICG-FA video recordings of patients who underwent esophagectomy with gastric conduit reconstruction between August 2020 until February 2022. Recordings were analyzed by two quantification software implementations: AMS and CPH. The quantitative parameter used to measure visceral perfusion was the normalized maximum slope derived from fluorescence time curves. The agreement between AMS and CPH was evaluated in a Bland-Altman analysis. The relation between the intraoperative measurement of perfusion and the incidence of anastomotic leakage was determined for both software implementations. Seventy pre-anastomosis ICG-FA recordings were included in the study. The Bland-Altman analysis indicated a mean relative difference of +58.2% in the measurement of the normalized maximum slope when comparing the AMS software to CPH. The agreement between AMS and CPH deteriorated as the magnitude of the measured values increased, revealing a proportional (linear) bias (R2=0.512, p<0.001). Neither the AMS nor the CPH measurements of the normalized maximum slope held a significant relationship with the occurrence of anastomotic leakage (median of 0.081 versus 0.074, p=0.32 and 0.041 vs 0.042, p=0.51, respectively). This is the first study to demonstrate technical differences in software implementations that can lead to discrepancies in ICG-FA quantification in human clinical cases. The possible variation among software-based quantification methods should be considered when interpreting studies that report quantitative ICG-FA parameters and derived thresholds, as there may be a limited external validity.

Experimental investigation of confinement effect in single molecule amplification via real-time digital PCR on a multivolume droplet array SlipChip

BackgroundDigital polymerase chain reaction (digital PCR) is an important quantitative nucleic acid analysis method in both life science research and clinical diagnostics. One important hypothesis is that by physically constraining a single nucleic acid molecule in a small volume, the relative concentration can be increased therefore further improving the analysis performance, and this is commonly defined as the confinement effect in digital PCR. However, experimental investigation of this confinement effect can be challenging since it requires a microfluidic device that can generate partitions of different volumes and an instrument that can monitor the kinetics of amplification. (96). ResultsHere, we developed a real-time digital PCR system with a multivolume droplet array SlipChip (Muda-SlipChip) that can generate droplet of 125 nL, 25 nL, 5 nL, and 1 nL by a simple “load-slip” operation. In the digital region, by reducing the volume, the relative concentration is increased, the amplification kinetic can be accelerated, and the time to reach the fluorescence threshold, or Cq value, can be reduced. When the copy number per well is much higher than one, the relative concentration is independent of the partition volume, thus the amplification kinetics are similar in different volume partitions. This system is not limited to studying the kinetics of digital nucleic acid amplification, it can also extend the dynamic range of the digital nucleic acid analysis by additional three orders of magnitude by combining a digital and an analog quantification algorithm. (140). SignificanceIn this study, we experimentally investigated for the first time the confinement effect in the community of digital PCR via a new real-time digital PCR system with a multivolume droplet array SlipChip (Muda-SlipChip). And a wider dynamic range of quantification methods compared to conventional digital PCR was validated by this system. This system provides emerging opportunities for life science research and clinical diagnostics. (63).

Aerobic Exercise Modulates Proteomic Profiles in Gastrocnemius Muscle of db/db Mice, Ameliorating Sarcopenia.

Type-2 diabetes mellitus (T2DM)-induced sarcopenia is intertwined with diminished insulin sensitivity and extracellular matrix (ECM) remodeling in skeletal muscle and other organs. Physical activities such as aerobic exercise play a crucial role in regulating blood glucose levels, insulin sensitivity, metabolic pathways, oxidative stress, fibrosis, ECM remodeling, and muscle regeneration by modulating differentially expressed protein (DEP) levels. The objectives of our research were to investigate the effect of six weeks of aerobic exercise on the gastrocnemius and soleus muscle of db/db mice's DEP levels compared to those of sedentary db/db mice. A total of eight db/db mice were divided into two groups (n = 4 per group), namely sedentary mice (SED) and exercise-trained mice (ET), of which the latter were subjected to six weeks of a moderate-intensity aerobic exercise intervention for five days per week. After the exercise intervention, biochemical tests, including analyses of blood glucose and HbA1c levels, were performed. Histological analysis using H & E staining on tissue was performed to compare morphological characters. Gastrocnemius and soleus muscles were dissected and processed for proteomic analysis. Data were provided and analyzed based on the DEPs using the label-free quantification (LFQ) algorithm. Functional enrichment analysis and Ingenuity Pathway Analysis (IPA) were employed as bioinformatics tools to elucidate the molecular mechanisms involved in the DEPs and disease progression. Significantly reduced blood glucose and HbA1c levels and an increased cross-sectional area (CSA) of gastrocnemius muscle fibers were seen in the ET group after the exercise interventions due to upregulations of metabolic pathways. Using proteomics data analysis, we found a significant decrease in COL1A1, COL4A2, ENG, and LAMA4 protein levels in the ET gastrocnemius, showing a significant improvement in fibrosis recovery, ECM remodeling, and muscle regeneration via the downregulation of the TGF-β signaling pathway. Upregulated metabolic pathways due to ET-regulated DEPs in the gastrocnemius indicated increased glucose metabolism, lipid metabolism, muscle regeneration, and insulin sensitivity, which play a crucial role in muscle regeneration and maintaining blood glucose and lipid levels. No significant changes were observed in the soleus muscle due to the type of exercise and muscle fiber composition. Our research suggests that engaging in six weeks of aerobic exercise may have a positive impact on the recovery of T2DM-induced sarcopenia, which might be a potential candidate for mitigation, prevention, and therapeutic treatment in the future.

Binary quantification and dataset shift: an experimental investigation

AbstractQuantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has been trained and the unlabelled data are not IID, i.e., suffer from dataset shift. To date, quantification methods have mostly been tested only on a special case of dataset shift, i.e., prior probability shift; the relationship between quantification and other types of dataset shift remains, by and large, unexplored. In this work we carry out an experimental analysis of how current quantification algorithms behave under different types of dataset shift, in order to identify limitations of current approaches and hopefully pave the way for the development of more broadly applicable methods. We do this by proposing a fine-grained taxonomy of types of dataset shift, by establishing protocols for the generation of datasets affected by these types of shift, and by testing existing quantification methods on the datasets thus generated. One finding that results from this investigation is that many existing quantification methods that had been found robust to prior probability shift are not necessarily robust to other types of dataset shift. A second finding is that no existing quantification method seems to be robust enough to dealing with all the types of dataset shift we simulate in our experiments. The code needed to reproduce all our experiments is publicly available at https://github.com/pglez82/quant_datasetshift.

Dual-Energy CT Material Decomposition: The Value in the Detection of Lymph Node Metastasis from Breast Cancer.

To evaluate the diagnostic performance of a dual-energy computed tomography (DECT)-based material decomposition algorithm for iodine quantification and fat fraction analysis to detect lymph node metastases in breast cancer patients. 30 female patients (mean age, 63.12 ± 14.2 years) diagnosed with breast cancer who underwent pre-operative chest DECT were included. To establish a reference standard, the study correlated histologic repots after lymphadenectomy or confirming metastasis in previous/follow-up examinations. Iodine concentration and fat fraction were determined through region-of-interest measurements on venous DECT iodine maps. Receiver operating characteristic curve analysis was conducted to identify the optimal threshold for differentiating between metastatic and non-metastatic lymph nodes. A total of 168 lymph nodes were evaluated, divided into axillary (metastatic: 46, normal: 101) and intramammary (metastatic: 10, normal: 11). DECT-based fat fraction values exhibited significant differences between metastatic (9.56 ± 6.20%) and non-metastatic lymph nodes (41.52 ± 19.97%) (p < 0.0001). Absolute iodine concentrations showed no significant differences (2.25 ± 0.97 mg/mL vs. 2.08 ± 0.97 mg/mL) (p = 0.7999). The optimal fat fraction threshold for diagnosing metastatic lymph nodes was determined to be 17.75%, offering a sensitivity of 98% and a specificity of 94%. DECT fat fraction analysis emerges as a promising method for identifying metastatic lymph nodes, overcoming the morpho-volumetric limitations of conventional CT regarding lymph node assessment. This innovative approach holds potential for improving pre-operative lymph node evaluation in breast cancer patients, offering enhanced diagnostic accuracy.

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

AbstractTraumatic brain injury (TBI) often results in midline shift (MLS) that is a critical indicator of the severity and prognosis of head injuries. Automated analysis of MLS from head computed tomography (CT) scans using artificial intelligence (AI) techniques has gained much attention in the past decade and has shown promise in improving diagnostic efficiency and accuracy. This review aims to summarize the current state of research on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed, evaluate the performance of the algorithms, and draw conclusions regarding their potential clinical applicability. A comprehensive literature search was conducted, identifying 15 distinctive publications. The identified articles were analyzed for their focus on MLS detection and quantification using AI techniques, including their choice of AI algorithms, dataset characteristics, and methodology. The reviewed articles covered various aspects related to MLS detection and quantification, employing deep neural networks trained on two-dimensional or three-dimensional CT imaging datasets. The dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity, with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%. AI-based approaches utilizing deep neural networks have demonstrated potential in the automated detection and quantification of MLS in TBI cases. However, different researchers have used different techniques; hence, critical comparison is difficult. Further research and standardization of evaluation protocols are needed to establish the reliability and generalizability of these AI algorithms for MLS detection and quantification in clinical practice. The findings highlight the importance of AI techniques in improving MLS diagnosis and guiding clinical decision-making in TBI management.

MAGIC: Malware behaviour analysis and impact quantification through signature co-occurrence and regression

Malware poses risks by compromising both data integrity and system security. Proactive defense efforts have led to the adoption of malware scoring, allowing analysts to assess the severity and develop countermeasures. These scores indicate the degree of malware maliciousness based on triggered signatures. However, current scoring methods do not precisely depict the true extent of malware's maliciousness. This inaccuracy is attributed to an inadequate assessment of the impact of behaviour corresponding to signatures on both system and network resources. To address this limitation, the paper proposes a novel scoring approach that accurately quantifies the impact of signatures triggered by malware through co-occurrence analysis. The method assesses the ensemble behaviour of signatures across two phases. In the first phase of signature scoring, an impact score quantification algorithm initializes each signature to predefined severity score bands based on the studied severity and frequency. The second phase refines initial scores iteratively, considering mutual information among signatures co-occurring in the malware's execution. Experimental results validate the proposed method's ability to accurately reflect signature maliciousness. This novel scoring method enhances malware analysis platforms in generating more precise scores compared to traditional methods, thereby improving resilience against evolving malware threats in the dynamic cybersecurity landscape.

Hemoperitoneum Quantification in Non-contrast CT: Evaluating Feasibility with the Novel HUVAO Segmentation Algorithm

Abstract Background Injuries involving substantial bleeding, frequently encountered in victims of road traffic accidents, pose a significant risk to mortality. For abdominal trauma cases, accurately assessing internal bleeding and hematomas becomes crucial. Detecting hemoperitoneum, which indicates both blood loss and organ damage in the abdominal cavity, requires precise evaluation. Timely diagnosis and quantification of hemoperitoneum following road accidents are crucial during the critical golden hour, enabling prioritized medical intervention and potentially saving lives while enhancing overall patient care. However, achieving precise hemoperitoneum quantification in abdominal trauma faces challenges due to the intricate nature of overlapping Hounsfield unit (HU) regions. Methods In this feasibility study, we sought to assess the efficacy of the novel HUVAO (Hounsfield Unit-based Volume quantification of Asymmetrical Objects) segmentation algorithm for quantifying hemoperitoneum in thoracoabdominal non-contrast computed tomography (CT) images. Using 28 retrospective non-contrast CT scans of thoracoabdominal regions from trauma patients, we analyzed crucial imaging data without necessitating additional scans or contrast-enhanced procedures. The study aimed to compare HUVAO against classical algorithms and visual estimations by trained radiologists for hemoperitoneum segmentation in thoracoabdominal non-contrast CT images. Results Our findings revealed that although the technical feasibility of employing HUVAO and other segmentation algorithms for hemoperitoneum quantification is evident, the outcomes derived from these algorithms display notable discrepancies. Conclusion In assessing technical feasibility, we introduced the HUVAO segmentation algorithm for hemoperitoneum quantification, comparing its performance against classical segmentation algorithms and visual estimations from trained radiologists. While our results affirm the technical feasibility of HUVAO for this purpose, the observed variations underscore the task's inherent complexity. This emphasizes the limitations of relying solely on HU-based detection, advocating for integration with clinical data. This insight urges exploration of advanced techniques to boost accuracy and elevate patient care standards.

Comparative study of the glistening between four intraocular lens models assessed by OCT and deep learning.

To evaluate the glistening in 4 different models of intraocular lenses (IOLs) using optical coherence tomography (OCT) and deep learning (DL). Centro Internacional de Oftalmología Avanzada (Madrid, Spain). Cross-sectional study. 325 eyes were assessed for the presence and severity of glistening in 4 IOL models: ReSTOR+3 SN6AD1 (n = 41), SN60WF (n = 110), PanOptix TFNT (n = 128) and Vivity DFT015 (n = 46). The presence of glistening was analyzed using OCT, identifying the presence of hyperreflective foci (HRF) in the central area of the IOL. A manual and an original DL-based quantification algorithm designed for this purpose was applied. Glistening was detected in 22 (53.7%) ReSTOR SN6AD1, 44 (40%) SN60WF, 49 (38.3%) PanOptix TFNT, and 4 (8.7%) Vivity DFT015 IOLs, when any grade was considered. In the comparison of the different types of IOLs, global glistening measured as total HRF was 17.3 ± 25.9 for the ReSTOR+3; 9.3 ± 15.7 for the SN60WF; 6.9 ± 10.5 for the PanOptix; and 1.2 ± 2.6 for the Vivity ( P < .05). There was excellent agreement between manual and DL-based quantification (≥0.829). It is possible to quantify, classify and compare the glistening severity in different IOL models using OCT images in a simple and objective manner with a DL algorithm. In the comparative study, the Vivity presented the lowest severity of glistening.

Voltammetric methods for electrochemical characterization and quantification of artemether-based antimalarials.

Every year substandard and falsified (SF) artemisinin derivative-based antimalarials are responsible for the loss of 450 000 deaths and billions of GBP. The lack of infrastructure and funds to support pharmaceutical quality control in many low-and-middle-income countries contributes to this problem. This work assesses fitness for purpose of voltammetric methods for identification and quantification of artemether in the presence of excipients. Electrochemical characterization of artemether using cyclic voltammetry shows that the reduction of artemether is chemically irreversible within the potential range of -0.4 V to -1.4 V. A chronocoulometric quantification algorithm for artemether is created and tested with pure artemether, as well as filtered and unfiltered Riamet® tablets. Filtration of Riamet® tablets provides no additional benefit for the quantification of artemether in Riamet®. In addition, artemether's response to pH indicates possible protonation and coupled homogeneous chemistry. Finally, sodium sulfite is an effective means of removing dissolved oxygen and improving artemether signal resolution in air-equilibrated PBS. This concludes that electrochemical analysis is a promising method for artemether identification and quantification.

A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

IntroductionThe Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.ObjectivesTo develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.MethodsAn automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.ResultsThe extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.ConclusionsThe extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Visual Analysis to Assess Attraction and Organisation of Contemporary Metropolitan Systems—A Case Study of Central and Northern Italy

The landscape of scientific research is rich with experiments aimed at identifying polycentric morphologies, defining their degree of polycentricity, and the socio-economic and environmental relationships that develop within them. However, some aspects are still under-researched, such as defining a procedure for determining the extent of the metropolitan area of influence. This research aims to experiment with a graphical–analytical methodology aimed at identifying and representing the functional area of metropolises, i.e., the territorial limit beyond which a metropolis’ attractiveness ceases to exert its territorial influence, and which also allows the territorial ramification of urban cores with greater attractiveness to be determined and visualized graphically. Using Visual Analytics as a conceptual basis, it is possible to combine methods and technologies that harness the potential of human understanding with the increased capabilities of electronic data processing for a more adequate understanding of the research scope. For this research, the graph-analytic mix adopted comprises the graph theory algorithms for the analytical quantification of relationships and the reference surface area of polycentric metropolises. In contrast, the subsequent visualisation of relationships and their spatial branching is based on the electronic evolution of graphical techniques based on the works of Henry Drury Harness and Charles Joseph Minard, particularly those invented to map commuter flows and migrations. The research results, focusing on Northern Italy, demonstrate a highly interconnected and polycentric system, with macro-areas whose functional boundaries seldom coincide with the administrative boundaries of the regions. The research demonstrates the existence of five territorial macro-regions encompassing the 11 regions outlined in the Italian state’s legislation, containing polycentric metropolitan systems with distributional characteristics that differ from each other.

Magnetstein: An Open-Source Tool for Quantitative NMR Mixture Analysis Robust to Low Resolution, Distorted Lineshapes, and Peak Shifts.

1H NMR spectroscopy is a powerful tool for analyzing mixtures including determining the concentrations of individual components. When signals from multiple compounds overlap, this task requires computational solutions. They are typically based on peak-picking and the comparison of obtained peak lists with libraries of individual components. This can fail if peaks are not sufficiently resolved or when peak positions differ between the library and the mixture. In this paper, we present Magnetstein, a quantification algorithm rooted in the optimal transport theory that makes it robust to unexpected frequency shifts and overlapping signals. Thanks to this, Magnetstein can quantitatively analyze difficult spectra with the estimation trueness an order of magnitude higher than that of commercial tools. Furthermore, the method is easier to use than other approaches, having only two parameters with default values applicable to a broad range of experiments and requiring little to no preprocessing of the spectra.

Machine Learning Algorithms for Intelligent Decision Recognition and Quantification of Cr(III) in Chromium Speciation.

Cr(III) is a common oxidation state of chromium, and its presence in the environment can occur naturally or as a result of human activities, such as industrial processes, mining, and waste disposal. This article explores the application of machine learning algorithms for the intelligent decision recognition and quantification of Cr(III) in chromium speciation. Three different machine learning models, namely, the Decision Tree (DT) model, the PCA-SVM (Principal Component Analysis-Support Vector Machine) model, and the LDA (Linear Discriminant Analysis) model, were employed and evaluated for accurate and efficient classification of chromium concentrations based on their fluorescence responses. Furthermore, stepwise multiple linear regression analysis was utilized to achieve a more precise quantification of trivalent chromium concentrations through fluorescence visualization. The results demonstrate the potential of machine learning algorithms in accurately detecting and quantifying Cr(III) in chromium speciation with implications for environmental and industrial applications in chromium detection and quantification. The findings from this research pave the way for further exploration and implementation of these models in real-world scenarios, offering valuable insights into various environmental and industrial contexts.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Data-informed uncertainty quantification for wave scattering by heterogeneous media

We present an efficient data-driven offline/online Bayesian algorithm for uncertainty quantification (UQ) in the induced scattered field when a time-harmonic incident wave interacts with an uncertain heterogeneous medium. The incident wave of interest need not be known in advance, and the uncertainty is informed by noisy scattering data obtained from other incident waves impinging on the medium. Our UQ algorithm is accelerated by a novel stochastic reduced order model (ROM) based on the T-matrix, and the ROM is independent of both the incident wave, and other incident waves used to generate the data. This important property allows the model to be set up offline.

An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis.

Vomocytosis is a process that occurs when internalized fungal pathogens escape from phagocytes without compromising the viability of the pathogen and the host cell. Manual quantification of time-lapse microscopy videos is currently used as the standard to study pathogen behavior and vomocytosis incidence. However, human-driven quantification of vomocytosis (and the closely related phenomenon, exocytosis) is incredibly burdensome, especially when a large volume of cells and interactions needs to be analyzed. In this study, we designed a MATLAB algorithm that measures the extent of colocalization between the phagocyte and fungal cell (Cryptococcus neoformans; CN) and rapidly reports the occurrence of vomocytosis in a high throughput manner. Our code processes multichannel, time-lapse microscopy videos of cocultured CN and immune cells that have each been fluorescently stained with unique dyes and provides quantitative readouts of the spatiotemporally dynamic process that is vomocytosis. This study also explored metrics, such as the rate of change of pathogen colocalization with the host cell, that could potentially be used to predict vomocytosis occurrence based on the quantitative data collected. Ultimately, the algorithm quantifies vomocytosis events and reduces the time for video analysis from over 1 h to just 10 min, a reduction in labor of 83%, while simultaneously minimizing human error. This tool significantly minimizes the vomocytosis analysis pipeline, accelerates our ability to elucidate unstudied aspects of this phenomenon, and expedites our ability to characterize CN strains for the study of their epidemiology and virulence.

OGC O08 Quantification of fluorescence angiography for visceral perfusion assessment in gastric conduit reconstruction: measuring agreement between two software algorithms

Abstract Background Building evidence suggests that fluorescence angiography is capable of reducing anastomotic leakage rates. Many software-based quantification algorithms have been developed aiming to obtain an objective measure for intraoperative visceral perfusion at the anastomotic site. Validation and comparison of these models in the current literature is scarce. The aim of this study was to measure agreement between two independently developed software algorithms for the quantitative assessment of visceral perfusion using indocyanine green fluorescence angiography. Methods 81 video recordings from patients who underwent fluorescence angiography-guided Ivor Lewis esophagectomy with gastric conduit reconstruction between August 2020 and February 2022 were available for analysis. In this dataset, we standardized patient, theatre, camera and imaging display variables. Recordings were analyzed retrospectively based on intraoperatively selected regions of interest using two software based-quantification algorithms (AMS and CPH). Quantitative parameters derived from the fluorescence time curve (FTC) were recorded. The comparison focused on the predefined primary outcome variable for quantitative perfusion assessment "normalized maximum slope". Agreement between the two software algorithms was evaluated in a Bland-Altman analysis. Quantitative parameters in patients with anastomotic leakage were compared to those without. Results Out of 81 video recordings, 70 could be processed using both algorithms. The output values for the normalized maximum slope from the AMS and the CPH software held a moderate-to-strong correlation in a linear regression model (R-squared: 0.544, p&amp;lt;0.001). The Bland-Altman plot indicated a mean±SD difference of 0.037±0.018 arbitrary units for the normalized maximum slope, with significant differences between the two software measurements (p&amp;lt;0.001). Proportional bias was observed using linear regression in the Bland-Altman plot (R-squared=0.715, p&amp;lt;0.001). Disparities between the two measurement methods became proportionally larger among higher mean values. Neither of the two software methods showed a significant difference in perfusion measurement between the anastomotic leakage and no leakage groups (p=0.32 and p=0.51). Differences in signal processing, such as smoothing techniques and window selection, were identified as potential factors contributing to the disparity in outcomes. Conclusions The two software-based quantification methods for visceral perfusion demonstrated significant differences. Varying agreement among quantification methods should be considered while interpreting studies that report quantitative parameters and derived thresholds for clinical endpoints, as there may be a limited external validity.

Reconfiguration and loss of peritubular capillaries in chronic kidney disease

Functional and structural alterations of peritubular capillaries (PTCs) are a major determinant of chronic kidney disease (CKD). Using a software-based algorithm for semiautomatic segmentation and morphometric quantification, this study analyzes alterations of PTC shape associated with chronic tubulointerstitial injury in three mouse models and in human biopsies. In normal kidney tissue PTC shape was predominantly elongated, whereas the majority of PTCs associated with chronic tubulointerstitial injury had a rounder shape. This was reflected by significantly reduced PTC luminal area, perimeter and diameters as well as by significantly increased circularity and roundness. These morphological alterations were consistent in all mouse models and human kidney biopsies. The mean circularity of PTCs correlated significantly with categorized glomerular filtration rates and the degree of interstitial fibrosis and tubular atrophy (IFTA) and classified the presence of CKD or IFTA. 3D reconstruction of renal capillaries revealed not only a significant reduction, but more importantly a substantial simplification and reconfiguration of the renal microvasculature in mice with chronic tubulointerstitial injury. Computational modelling predicted that round PTCs can deliver oxygen more homogeneously to the surrounding tissue. Our findings indicate that alterations of PTC shape represent a common and uniform reaction to chronic tubulointerstitial injury independent of the underlying kidney disease.