Digital Image Analysis Research Articles

The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis

Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that correlate with liver fat. The miRNAs were analyzed by miRNAseq in liver samples from two cohorts of patients with a precise quantification of liver steatosis. Common miRNAs showing correlation with liver steatosis were validated by RT-qPCR in paired liver and serum samples. Multivariate models were built using partial least squares (PLS) regression to predict the percentage of liver steatosis from serum miRNA levels. Leave-one-out cross validation and external validation were used for model selection and to estimate predictive performance. The miRNAseq results disclosed (a) 144 miRNAs correlating with triglycerides in a set of liver biobank samples (n = 20); and (b) 124 and 102 miRNAs correlating with steatosis by biopsy digital image and MRI analyses, respectively, in liver samples from morbidly obese patients (n = 24). However, only 35 miRNAs were common in both sets of samples. RT-qPCR allowed to validate the correlation of 10 miRNAs in paired liver and serum samples. The development of PLS models to quantitatively predict steatosis demonstrated that the combination of serum miR-145-3p, 122-5p, 143-3p, 500a-5p, and 182-5p provided the lowest root mean square error of cross validation (RMSECV = 1.1, p-value = 0.005). External validation of this model with a cohort of mixed MASLD patients (n = 25) showed a root mean squared error of prediction (RMSEP) of 5.3. In conclusion, it is possible to predict the percentage of hepatic steatosis with a low error rate by quantifying the serum level of five miRNAs using a cost-effective and easy-to-implement RT-qPCR method.

Histopathological substrate of increased T2 signal in the anterior temporal lobe white matter in temporal lobe epilepsy associated with hippocampal sclerosis.

This study was undertaken to analyze the histology underlying increased T2 signal intensity (iT2SI) in anterior temporal lobe white matter (aTLWM) epilepsy due to hippocampal sclerosis (TLE/HS). Twenty-three patients were included: 16 with increased T2 signal in the aTLWM and seven with HS only. Magnetic resonance imaging (MRI) findings were consistent across two neuroradiologists (kappa = .89, p < .001). Quantification of neuronal cells, astrocytes, oligodendrocytes, and vacuolization in the white matter of temporal lobe specimens was performed by immunohistochemistry (neuronal nuclear antigen, glial fibrillary acidic protein, oligodendrocyte transcription factor, and basic myelin protein, respectively). Surgical specimens from TLE/HS patients with and without iT2SI in the aTLWM were compared. Samples of aTLWM were divided into three groups, according to MRI features: G1 = samples of iT2SI, G2 = samples with normal T2 signal intensity from patients without white matter imaging abnormalities, and G3 = samples with normal T2 signal intensity adjacent to areas with iT2SI. Patients with increased T2 signal had a significantly younger age at epilepsy onset (p < .035). Histological analysis revealed a higher percentage of vacuolar area in these patients (p < .004) along with a lower number of ectopic neurons (p = .042). No significant differences were found in astrocyte or oligodendrocyte counts among groups. A higher proportion of vacuoles in regions with iT2SI may be the histopathologic substrate of this signal alteration in the white matter of the temporal lobe in patients with TLE/HS. This method of quantifying vacuoles using digital image analysis proved reliable and cost-effective.

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis. Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample. A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database. The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

High α-SMA expression in the tumor stroma is associated with adverse clinical parameters in mismatch repair-proficient colorectal cancers only.

As mismatch repair status confers differential prognosis in colorectal cancers, this study aimed to determine associations of α-smooth muscle actin (α-SMA) protein expression in mismatch repair-proficient (pMMR) and mismatch repair-deficient (dMMR) colorectal tumors with clinicopathologic and prognostic features. Tissue microarrays from patients with colorectal cancer, immunostained with α-SMA, were assessed through digital image analysis. Total (n=962), pMMR (n=782), and dMMR (n=156) stromal H-scores were assessed for associations with clinicopathologic and survival data. Higher α-SMA expression was correlated with pMMR status (P=5.2223×10-8). In the pMMR subgroup, higher α-SMA stromal expression at the tumor periphery was correlated with higher T stage (P=.002), perineural invasion (P=.038), infiltrative tumor edge (P=.01), involved nodal status (P=.036), metastases (P=.013), synchronous metastases (P=.007), recurrence (P=.004), and both 3-year and 5-year survival (P=.018). dMMR tumors showed no significant correlations with α-SMA staining. The findings highlight that immunostaining with α-SMA in pMMR colorectal tumors, especially at the tumor periphery, has the potential to identify patients with adverse prognostic features. Digital assessment of α-SMA may offer improved objectivity, accuracy, economy of time, and risk stratification for management.

Novel optical optode for selective detection and removal of ultra-trace level of mercury ions in different environmental real samples

Owing to the high cost and unavailability of different analytical techniques, there is an urgent need to develop new techniques not only for detecting but also removing mercury ions in real samples. Thus, an optical chemical sensor based on the anchoring of phenanthraquinone monophenylthiosemicarbazone in a plasticized cellulose triacetate membrane was fabricated and applied to the recognition and removal of mercury ions from aqueous solutions. The synthesized optode was characterized by FT-IR, SEM, AFM, and thermal analysis. Several parameters, including the pH, temperature, contact time, washing solvent, and washing time, were optimized. Under optimal conditions, a promising optode film platform was utilized for sensing mercury ions, and the concentrations were calculated based on colorimetric analysis (Histogram, RGB) of digital images, visualization, and spectrophotometry. Also, an optical optode was used for complete adsorption of mercury ions from aqueous solutions. In addition, the regeneration of the synthesized optode was evaluated using 0.1 mol L− 1 nitric acid, which effectively removed all adsorbed mercury ions. The obtained data indicated good linearity in the sensing and adsorption of Hg2+ over a concentration range of 0.005–5000 µgL− 1 with a low limit of detection (LOD = 0.066 µgL− 1) and limit of quantification (LOQ, 0.22 µgL− 1). Furthermore, it showed good distinctions in the presence of coexisting ions, high stability (five months), good applicability, and reproducibility (RSD = 1.31%), making it a promising sensor for Hg2+ detection. On the other hand, the kinetic studies revealed that the pseudo-second-order was the best model for describing the adsorption behavior of mercury ions on the optode surface. Also, the thermodynamic parameters indicate spontaneous (ΔG0 < 0) and endothermic (ΔH0 < 0) reactions. Also, the maximum adsorption capacity was found to be 73.2 mg g− 1. Thus, the optodes were successfully applied for the detection and/or removal of Hg2+ in different real samples, including cucumber, fish, soil, and water samples, with excellent recoveries of 98.1–99.5%.

Automatic System for Acquisition and Analysis of Microscopic Digital Images Containing Activated Sludge

Automatic System for Acquisition and Analysis of Microscopic Digital Images Containing Activated Sludge

Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer.

To test the efficacy of artificial intelligence (AI)-assisted Ki-67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance. This study used 494 cases of Ki-67 slide images of IBC core needle biopsies. The methods were divided into two steps: (i) construction of a deep-learning model (DL); and (ii) DL implementation for Ki-67 analysis. First, a DL tissue classifier model (DL-TC) and a DL nuclear detection model (DL-ND) were constructed using HALO AI DenseNet V2 algorithm with 31,924 annotations in 300 Ki-67 digital slide images. Whether the class predicted by DL-TC in the test set was correct compared with the annotation of ground truth at the pixel level was evaluated. Second, DL-TC- and DL-ND-assisted digital image analysis (DL-DIA) was performed in the other 194 luminal-type cases and correlations with manual counting and clinical outcome were investigated to confirm the accuracy and prognostic potential of DL-DIA. The performance of DL-TC was excellent and invasive carcinoma nests were well segmented from other elements (average precision: 0.851; recall: 0.878; F1-score: 0.858). Ki-67 index data and the number of nuclei from DL-DIA were positively correlated with data from manual counting (ρ = 0.961, and 0.928, respectively). High Ki-67 index (cutoff 20%) cases showed significantly worse recurrence-free survival and breast cancer-specific survival (P = 0.024, and 0.032, respectively). The performances of DL-TC and DL-ND were excellent. DL-DIA demonstrated a high degree of concordance with manual counting of Ki-67 and the results of this approach have prognostic potential.

Electroextraction of Large Volume Samples Using Paper Points Coupled With Hollow Fiber Membranes: Study of Parameters and Strategies to Enhance Analytical Performance.

Electroextraction (EE) encompasses a range of sample preparation methods whose effectiveness, selectivity, and efficiency are significantly influenced by the physical-chemical characteristics of analytes, samples, and instrumental conditions. This article explores, for the first time, various strategies aimed at enhancing the extraction efficiency of a recent approach of EE utilizing a paper point (PP) combined with a hollow fiber (HF) (abbreviated as PP-HF-EE) to extract various cationic and anionic model compounds from water samples. The study also explores, experimentally, the impact of agitation, organic filter composition, PP diameter, and PP brand on extraction performance, and proves that all these factors are quite important, especially when digital image analysis is utilized for determination. Furthermore, this work demonstrates the ease and feasibility of simultaneously extracting cations and anions using PP-HF-EE and proposes a straightforward method to enhance analyte concentration on the vertex of the PP through a base-to-vertex focusing. Lastly, it is demonstrated, using capillary electrophoresis coupled to a UV-Vis detector, that for PP-HF-EE, the extraction efficiency and pre-concentration factor are less dependent on other parameters when multiple PPs per sample are utilized, with signal enhancement values of up to 111 and 339 for nortriptyline and haloperidol, respectively. All the findings and strategies presented herein constitute significant contributions that can facilitate future research in method development, particularly in the utilization of PP-HF-EE and similar EE approaches.

Agricultural spray drone deposition, Part 2: Operational height and nozzle influence pattern uniformity, drift, and weed control

Abstract Agricultural spray drone (ASD) use in managed turfgrass has been given limited attention in the scientific literature. Further, deposition patterns of ASD spray have been obscured in previous research by ambient wind, crop canopy interference, and limited sampling resolution. Using a continuous sampling method involving blue colorant and water sprayed over white Kraft paper that was assessed via digital image analysis of stain objects and referenced spectrophotometric analysis of extractant, deposition metrics were estimated across a 29.3-m transect perpendicular to an ASD or ground sprayer spray swath. The ASD applies very fine droplets that are highly concentrated with herbicide, similar to ultra-low volume treatments, that improved smooth crabgrass [Digitaria ischemum (Schreb.) Schreb. ex Muhl.] control compared to a ground sprayer when the ASD was operated at 2 m above the turf. Unfortunately, these very fine droplets also drift, leading to four times greater droplet density at distance of almost 12 m away from the targeted spray swath following an operational height of 10 m compared to that at 2 m. As ASD operational height increases, drift and effective swath width at 30% coefficient of variation uniformity increases while effective application rate, total deposition, and D. ischemum control by quinclorac herbicide decreased. Total deposition decreased 6% for each m increase in ASD operational height, likely due to evaporation. The potential losses due to evaporation is a serious consideration for ASD use that has received little attention in the scientific literature. Our data suggest that ASD operational height should be as low as possible, but modification of spray systems may be needed to improve homogeneity of spray pattern.

A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.

Manual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, we recently designed an artificial intelligence based digital pathology system (AI-DP) for digital image capture and analysis of KK thick smears. Preliminary results of its diagnostic performance are encouraging, and a comprehensive evaluation of this technology as a cost-efficient end-to-end diagnostic to inform STH control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation. Here, we describe the study protocol for a comprehensive evaluation of the AI-DP based on its (i) diagnostic performance, (ii) repeatability/reproducibility, (iii) time-to-result, (iv) cost-efficiency to inform large-scale deworming programs, and (v) usability in both laboratory and field settings. For each of these five attributes, we designed separate experiments with sufficient power to verify the non-inferiority of the AI-DP (KK2.0) over the manual screening of the KK stool thick smears (KK1.0). These experiments will be conducted in two STH endemic countries with national deworming programs (Ethiopia and Uganda), focussing on school-age children only. This comprehensive study will provide the necessary data to make an evidence-based decision on whether the technology is indeed performant and a cost-efficient end-to-end diagnostic to inform large-scale deworming programs against STHs. Following the protocolized collection of high-quality data we will seek approval by WHO. Through the dissemination of our methodology and statistics, we hope to support additional developments in AI-DP technologies for other neglected tropical diseases in resource-limited settings. The trial was registered on September 29, 2023 Clinicaltrials.gov (ID: NCT06055530).

Digital image analysis of gas bypassing and mixing in gas‐fluidized bed: Effect of particle shape

AbstractThe study investigates effect of particle shape on gas bypassing and mixing of gas‐fluidized Geldart A particles. A shallow fluidized bed (FB), configured at benchscale, was used with digital image analysis (DIA) for the investigation. The extent of scatter of tracer particles throughout the bed was assessed from DIA images of defluidized powder. A novel method employing Jupyter notebook software, was used to directly determine Mixing Index from digital images. Remarkably, platelet‐shaped China clay powder displayed the best mixing characteristics (Mixing Index: 0.79) with no significant bypassing. Angular shaped Quartz displayed moderate mixing (Mixing Index: 0.67), but high bypassing (Bypassing Index: 0.75). Contrary to conventional assumptions, spherical‐shaped diatomite exhibited poor mixing (Mixing Index: 0.61) with the highest bypassing (Bypassing Index: 0.82). Platelet particles performed well even with fines removal. Most likely, particle shape significantly influenced the number of available particle contact points, tracer migration, and traceronparticle binding.

Quantitative anatomy of the infraspinatus muscle in the human fetus.

The study presents one of the six scapulohumeral muscles, which occupies most of the osteofibrous infraspinatus compartment. Along with the supraspinatus, teres minor and subscapularis muscles, the infraspinatus muscle contributes to the rotator cuff. It protects the posterior aspect of the articular capsule of the shoulder joint, adducts and externally rotates the arm. The aim of the study was to perform the quantitative analysis of the infraspinatus muscle in human fetuses and to elaborate growth dynamics for its morphometric parameters. Using anatomical dissection, digital image analysis (NIS Elements AR3.0) and statistics (Student's t-test, regression analysis), the vertical, transverse and oblique diameters, muscle circumference and projection surface area of the infraspinatus muscle were measured in 36human fetuses of both sexes (17♂, 19♀) aged 18-30weeks. The infraspinatus muscle revealed neither sex nor laterality differences. All examined morphometric parameters of the infraspinatus muscle increased commensurately in accordance with the following linear functions: y=-4.024 + 0.903×Age ± 0.621 (R²=0.96) for transverse diameter, y=-3.089 + 1.321×Age ± 0.897 (R²=0.97) for vertical diameter, y=-1.161 + 0.632×Age ± 0.444 (R²=0.97) for oblique diameter, y=-13.575 + 3.851×Age ± 1.938 (R²=0.98) for muscle circumference and y=-293.512 + 23.228×Age ±19.650 (R²=0.95) for projection surface area.

Associations of prostate tumor immune landscape with vigorous physical activity and prostate cancer progression.

Vigorous physical activity has been associated with lower risk of fatal prostate cancer. However, mechanisms contributing to this relationship are not understood. We studied 117 men with prostate cancer in the University of North Carolina Cancer Survivorship Cohort (UNC CSC) who underwent radical prostatectomy, and 101 radiation-treated prostate cancer patients in FASTMAN. Structured questionnaires administered in UNC CSC assessed physical activity. In both studies, digital image analysis of H&E-stained tissues was applied to quantify Tumor Infiltrating Lymphocytes (TILs) in segmented regions. Nanostring gene expression profiling in UNC CSC and microarray in FASTMAN were performed on tumor tissue and a 50-gene signature utilized to predict immune cell types. Vigorous recreational activity, reported by 34 (29.1%) UNC men, was inversely associated with TILs abundance. Tumors of men reporting any vigorous activity versus none showed lower gene expression-predicted abundance of Th, exhausted CD4 T cells and macrophages. T cell subsets, including Treg, Th, Tfh, exhausted CD4 T cells, and macrophages were associated with increased risk of biochemical recurrence, only among men with ERG-positive tumors. Vigorous activity was associated with lower prostate tumor inflammation and immune microenvironment differences. Macrophages and T cell subsets, including those with immunosuppressive roles and those with lower abundance in men reporting vigorous exercise, were associated with worse outcomes in ERG-positive prostate cancer. Our novel findings contribute to our understanding of the role of the tumor immune microenvironment in prostate cancer progression, and may provide insight into how vigorous exercise could affect prostate tumor biology.

Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology

The incorporation of digital pathology in clinical practice will require the training of pathologists in digital skills. Our study aimed to assess the reliability among pathologists in determining tumor percentage in whole slide images (WSI) of non-small cell lung cancer (NSCLC) using digital image analysis, and study how the results correlate with the molecular findings. Pathologists from nine centers were trained to quantify epithelial tumor cells, tumor-associated stromal cells, and non-neoplastic cells from NSCLC WSI using QuPath. Then, we conducted two consecutive ring trials. In the first trial, analyzing four WSI, reliability between pathologists in the assessment of tumor cell percentage was poor (intraclass correlation coefficient (ICC) 0.09). After performing the first ring trial pathologists received feedback. The second trial, comprising 10 WSI with paired next-generation sequencing results, also showed poor reliability (ICC 0.24). Cases near the recommended 20% visual threshold for molecular techniques exhibited higher values with digital analysis. In the second ring trial reliability slightly improved and human errors were reduced from 5.6% to 1.25%. Most discrepancies arose from subjective tasks, such as the annotation process, suggesting potential improvement with future artificial intelligence solutions.

Precision and speed: The AI revolution in oral squamous cell carcinoma detection

Oral Squamous Cell Carcinoma (OSCC) is a highly aggressive tumor with a poor prognosis and is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. AI can precisely analyze a vast dataset of various imaging modalities, such as fluorescent, hyperspectral, cytological, histological, radiological, endoscopic, clinical, and infrared thermal modalities. In this review, we discuss digital histopathological image analysis, computer vision and radiological analysis for the early detection of OSCC.

Digital image analysis of vertebral body L4 and its ossification center in the human fetus.

Using a Siemens-Biograph 128 mCT camera the morphometric analysis of the L4 vertebral body and its ossification center were done in 55human fetuses aged 17 to 30weeks. No sex differences were found. The mean height, transverse and sagittal diameters of L4 vertebral body followed the logarithmic functions: y = -11.797+ 5.208 × ln(age) ± 0.372, y = -23.462 + 9.428 × ln(age) ± 0.702, y = 2.770 + 13.521 × ln(age) ± 1.722, respectively. The mean cross-sectional area of L4 vertebral body followed the linear function: y = -30.683 + 1.976 × age ± 2.701. The mean volume of L4 vertebral body followed the second-degree polynomial function: y = -93.983+ 0.385 × (age)² ± 23.707. The mean transverse and sagittal diameters of the ossification center of L4 vertebral body followed the natural logarithmic function: y = -27.106 + 10.178 × ln(age) ± 0.769 and y = -13.345 + 5.458 × ln(age) ± 0.424, respectively. The mean cross-sectional area and the volume of the ossification center of L4 vertebral body followed the linear function: y = -30.683 + 1.976 × age ± 2.701 and y = -43.214 + 2.760 × age ± 4.085, respectively.

Evaluation for coarse aggregate distribution of asphalt mixtures based on the two-dimensional digital image analysis

Meso-structure is one of the major sources of macro-performance in asphalt mixtures. The research on aggregate distribution state is of great significance for the further improvement of asphalt mixture macro-performance. However, the variations of research methods among prior works hamper the deduction of the holistic potential and promising directions of coarse aggregate distribution analysis from current research findings. This work comprehensively verified the quantifying ability of aggregate distribution state indices and their correlation with rutting performance under an advanced digital image processing technique and a wide range of asphalt mixture types, and aims to provide a reference of the holistic potential and promising directions of coarse aggregate distribution analysis for subsequent research. To this end, eleven typical asphalt mixtures’ aggregate distribution state and rutting performance were measured, quantified, and correlated. The results show that Kpe nears or more than 0.56 can be the sign of coarse aggregates effectively interlocked while it nears or less than 0.26 is the opposite; Uarea nears or less than 2 % can be the sign of reaching a coarse aggregate distribution equilibrium; Δorien approaches 6.21 can be the sign of reaching a coarse aggregate orientation equilibrium; mixtures with a nominal maximum aggregate size of 10 mm are disabled in gathering contact chains. Moreover, these coarse aggregate distribution state quantifying indices proposed in existing research can indeed describe the meso-structural features of asphalt mixtures to a certain extent. However, these indices fail to capture the underlying meso-structural features relating to rutting performance and lead to limited applicability. Furthermore, the poor performance of these coarse aggregate distribution state quantifying indices in the global fitting mainly comes from their incompatibility with changes in nominal maximum aggregate size.

A Digital Image Steganographic Detection Method for LSB Steganography

With the advancement of communication technology, communication information can no longer be transmitted merely through text but can be covertly conveyed via various types of communication carriers like images, audio, and video. This brings forth new challenges to traditional detection techniques that mainly target text information. Digital image steganalysis aims to detect and uncover communication information by recognizing steganographic behaviors contained within digital image files. This paper takes digital image steganographic activities based on least significant bit (LSB) as the main research object and conducts analysis and identification of digital images with steganographic information through visual attack method. It has been verified through experiments that steganographic activities based on LSB are hardly detectable through direct visual observation, but can be identified visually after undergoing visual attack processing.

Implementation of multiobjective decision-making algorithms and image analysis in HPTLC-guided extraction optimization of natural products

A new, efficient, and low-cost approach for monitoring extraction optimization was proposed based on high-performance thin-layer chromatography (HPTLC) coupled with digital image analysis. Since HPTLC produces rich chromatographic signals corresponding to various analytes which may be differently affected by extraction conditions, four multicriteria decision-making (MCDM) techniques were compared for their ability to aggregate multiple chromatographic responses: Derringer's desirability approach, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-2), and the Sum of ranking differences (SRD). Ultrasound-assisted extraction (UAE) of green tea leaves with ethanol-water mixtures was used as a model system. The amount of ethanol and extraction time were varied according to the central composite design. Ranking eleven extracts by Derringer's desirability approach, TOPSIS, and PROMETHEE-2 showed the same results. SRD analysis yielded slightly different results from previous methods. Response surface models based on the previous three MCDM approaches demonstrated that extraction conditions with moderate amounts of ethanol (73%) and extraction times (46 min) lead to optimal chromatographic profiles. Optimization performed on individual peaks tentatively corresponding to rutin, chlorophyll, and gallic acid may lead to different results. Aside from natural products, the proposed approach has the potential to be implemented in various extraction optimizations.

Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent

Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&amp;E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists.