Over 30 Years of DiI Use for Human Neuroanatomical Tract Tracing: A Scoping Review.

In the present study, we conducted a scoping review to provide an overview of the existing literature on the carbocyanine dye DiI, in human neuroanatomical tract tracing. The PubMed, Scopus, and Web of Science databases were systematically searched. We identified 61 studies published during the last three decades. While studies incorporated specimens across human life from the embryonic stage onwards, the majority of studies focused on adult human tissue. Studies that utilized peripheral nervous system (PNS) tissue were a minority, with the majority of studies focusing on the central nervous system (CNS). The most common topic of interest in previous tract tracing investigations was the connectivity of the visual pathway. DiI crystals were more commonly applied. Nevertheless, several studies utilized DiI in a paste or dissolved form. The maximum tracing distance and tracing speed achieved was, respectively, 70 mm and 1 mm/h. We identified studies that focused on optimizing tracing efficacy by varying parameters such as fixation, incubation temperature, dye re-application, or the application of electric fields. Additional studies aimed at broadening the scope of DiI use by assessing the utility of archival tissue and compatibility of tissue clearing in DiI applications. A combination of DiI tracing and immunohistochemistry in double-labeling studies have been shown to provide the means for assessing connectivity of phenotypically defined human CNS and PNS neuronal populations.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing.

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

The Biomedical Engineering Labor Market in Greece: A Survey Investigating Job Outlook, Satisfaction and Placement.

In this study, we have evaluated the real-world conditions, the job outlook and the job satisfaction in the Biomedical Engineering (BME) sector in Greece on the basis of the experience of about 12% of the graduates of the BME Department of the University of West Attica, Greece. An anonymous online questionnaire, implemented on the Microsoft Forms platform using multiple choice questions, short text answers and Likert-based scales, became publicly available to the graduates of the BME department. About 12% of the department's graduates responded to the survey. Results show that the time to first employment is very fast for both men and women. About 51.4% of men and 69.4% of women find their first job employment in the BME sector even before their graduation. The internship is considered important for first job placement by more than 50.6% of participants. BME jobs are perceived as most interesting (73.6%), in a good environment (71.9%), with satisfactory career prospects (45.9%), with satisfactory monthly net salary (44.2%) and satisfactory working hours (52.8%). Men are mostly employed in Service (40.5%), whereas women are mostly employed in Sales (33.3%). Most graduates with BSc degree are employed in Service (39.1%) and Sales (21.8%), most graduates with MSc degree are employed in Service (34.6%) and Hospitals/Health care centers (21.2%), and most graduates with PhD degree are employed in Academia and R&D (62.5%). Most well-paid participants (>1500 euros net salary) were PhD holders (71.5%), followed by MSc holders (25%) and BSc holders (16.2%). Maximum monthly salaries were found for those with more than 10 years of experience. In terms of BME sector, most well-paid participants (>1500 euros monthly net salary) are those working with R&D (86.7%), Sales (86.7%) and Management (60%). There is a high demand for biomedical engineers in the labor market in Greece, despite the continuing economic recession that the country is suffering from the past 12 years.

Employing machine learning and microscopy images of AIB1-stained biopsy material to assess the 5-year survival of patients with colorectal cancer.

Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC-patient 5-year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross-validation method for boosting the accuracy of predicting 5-year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5-year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5-year non-survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5-year survivors and non-survivors while the DL system displayed superior accuracy by employing DL features.

GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix.

The aim of the present study was to design an adaptable pattern recognition (PR) system to discriminate low- from high-grade squamous intraepithelial lesions (LSIL and HSIL, respectively) of the cervix using microscopy images of hematoxylin and eosin (H&E)-stained biopsy material from two different medical centers. Clinical material comprised H&E-stained biopsies of 66 patients diagnosed with LSIL (34 cases) or HSIL (32 cases). Regions of interest were selected from each patient's digitized microscopy images. Seventy-seven features were generated, regarding the texture, morphology and spatial distribution of nuclei. The probabilistic neural network (PNN) classifier, the exhaustive search feature selection method, the leave-one-out (LOO) and the bootstrap validation methods were used to design the PR system and to assess its precision. Optimal PR system design and evaluation were made feasible by the employment of graphics processing unit (GPU) and Compute Unified Device Architecture (CUDA) technologies. The accuracy of the PR-system was 93% and 88.6% when using the LOO and bootstrap validation methods, respectively. The proposed PR system for discriminating LSIL from HSIL of the cervix was designed to operate in a clinical environment, having the capability of being redesigned when new verified cases are added to its repository and when data from other medical centers are included, following similar biopsy material preparation procedures.

Assessment of HPV Risk Type in H&E-stained Biopsy Specimens of the Cervix by Microscopy Image Analysis.

OBJECTIVES: The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (LR) human papillomavirus virus (HPV) types and (b) to assess the HPV risk type by designing a decision-support system (DSS). MATERIALS AND METHODS: Clinical material comprised H&E-stained biopsies from squamous intraepithelial lesions of 55 patients with polymerase chain reaction-verified HR-HPV (26 patients) or LR-HPV (29 patients) infection. From each patient's biopsy specimen, we digitized 1 region of interest, guided by the expert physician. After the segmentation of nuclei, we quantified from each nucleus 77 textural and morphologic features. We represented each patient by a 77-feature vector, the feature means of all nuclei, and we created 2 classes for HR-HPV and LR-HPV types. We carried out (a) a statistical analysis to determine features with statistically significant differences between the 2 classes and (b) a discriminant analysis, by designing a DSS, to estimate the HPV risk type. RESULTS: Statistical analysis revealed 40 features with between-classes statistically significant differences and discriminant analysis showed that the best DSS design achieved a high accuracy of about 93% in identifying the HPV risk type on data not used in the design of the DSS. CONCLUSIONS: Nuclei of HR-HPV types were of higher intensity, contained larger structures, had higher edges, were coarser, rougher, had higher contrast, were larger, and attained more irregular shapes. The proposed DSS indicates that discrimination of HPV risk type from images of H&E-stained biopsy material of the cervix is promising.

Correlating Changes in the Epithelial Gland Tissue With Advancing Colorectal Cancer Histologic Grade, Using IHC Stained for AIB1 Expression Biopsy Material.

OBJECTIVE: The objective of this study was to study the textural and color changes occurring in the epithelial gland tissue with advancing colorectal cancer (CRC), utilizing immunohistochemical stain for AIB1 expression biopsy material. MATERIAL AND METHODS: Clinical material comprised biopsy specimens of 67 patients with a diagnosis of CRC. Two experienced pathologists used H&E-stained material for grading CRC lesions and immunohistochemical (IHC) stain for AIB1 expression. Twenty six patients were diagnosed with grade I, 28 with grade II, and 13 with grade III CRC. Guided by pathologists, we selected the regions of interest from AIB1-digitized images of each patient, encompassing the epithelial gland, and we computed 69 features, quantifying textural and color properties of the AIB1-stained lesions. We evaluated the statistical differences between grades by means of the Wilcoxon statistical test for each feature, and we assessed changes in feature values with advancing tumor grade by means of the Point Biserial Correlation. RESULTS: Statistical analysis revealed 14 single features, quantifying textural and color properties of the epithelial gland, which sustained statistically significant differences between LG-CRC and HG-CRC cases. These features were drawn from the gray-level image histogram, the cooccurrence matrix, the run length matrix, the discrete wavelet transform, the Tamura method, and the L*a*b color transform. CONCLUSIONS: A systematic statistical analysis of AIB1-stained biopsy material showed that high-grade CRC lesions contain higher intensity levels, appear coarser, are more homogeneous with smooth variation across the image, have lower contrast that is slowly varying across the image, have lower AIB1 staining, and have lower edges. A combination of textural and color attributes, evaluating image gray-tone distribution, textural roughness, inhomogeneity, AIB1 staining, and image coarseness should be considered in evaluating AIB1-stained CRC lesions.

Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade.

Background: Cervical dysplasia is a precancerous condition, and if left untreated, it may lead to cervical cancer, which is the second most common cancer in women. The purpose of this study was to investigate differences in nuclear properties of the H&E-stained biopsy material between low CIN and high CIN cases and associate those properties with the CIN grade. Methods: The clinical material comprised hematoxylin and eosin- (H&E-) stained biopsy specimens from lesions of 44 patients diagnosed with cervical intraepithelial neoplasia (CIN). Four or five nonoverlapping microscopy images were digitized from each patient's H&E specimens, from regions indicated by the expert physician. Sixty-three textural and morphological nuclear features were generated for each patient's images. The Wilcoxon statistical test and the point biserial correlation were used to estimate each feature's discriminatory power between low CIN and high CIN cases and its correlation with the advancing CIN grade, respectively. Results: Statistical analysis showed 19 features that quantify nuclear shape, size, and texture and sustain statistically significant differences between low CIN and high CIN cases. These findings revealed that nuclei in high CIN cases, as compared to nuclei in low CIN cases, have more irregular shape, are larger in size, are coarser in texture, contain higher edges, have higher local contrast, are more inhomogeneous, and comprise structures of different intensities. Conclusion: A systematic statistical analysis of nucleus features, quantified from the H&E-stained biopsy material, showed that there are significant differences in the shape, size, and texture of nuclei between low CIN and high CIN cases.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

OBJECTIVE: The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. MATERIAL AND METHODS: Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. RESULTS: Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. CONCLUSION: The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.